Nasal biomarkers of asthma

ABSTRACT

Asthma is a common, under-diagnosed disease affecting all ages. Mild to moderate asthma is particularly difficult to diagnose given currently available tools. A nasal biomarker of asthma is of high interest given the accessibility of the nose and shared airway biology between the upper and lower respiratory tract. A machine learning pipeline identified an asthma gene panel of 275 unique nasally-expressed genes interpreted via different classification models. This asthma gene panel can be utilized to reliably diagnose asthma in patients, including mild to moderate asthma, in a non-invasive manner and to distinguish asthma from other respiratory disorders, allowing appropriate treatment of the patient&#39;s asthma.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No. 62/296,291, filed on 17 Feb. 2016 and 62/296,915, filed on 18 Feb. 2016, the disclosures of each of which are herein incorporated by reference in their entirety.

GOVERNMENT SPONSORSHIP

This invention was made with government support under Grant Nos. R01GM114434, K08AI093538 and R01AI118833, all awarded by the National Institutes of Health (NIH). The government has certain rights in the invention.

BACKGROUND OF THE INVENTION 1. Field of the Invention

Embodiments of the present invention relate generally to methods for diagnosis and monitoring of asthma, including but not limited to mild to moderate asthma, and its differentiation from other respiratory disorders by determining the expression profiles of asthma-specific genes in nasal brushing samples.

2. Background

Asthma is a chronic respiratory disease that affects 8.6% of children and 7.4% of adults in the United States¹. The true prevalence of asthma may be higher than these estimates. In one study of US middle school children, 11% reported physician-diagnosed asthma with current symptoms, while an additional 17% reported active asthma-like symptoms without a diagnosis of asthma². Undiagnosed asthma leads to missed school and work, restricted activity, emergency department visits, and hospitalizations^(2, 3). Mild to moderate asthma in particular can be difficult to diagnose, as it intrinsically involves fluctuating symptoms and signs⁴. The airflow obstruction, bronchial hyper-responsiveness and airway inflammation that characterize asthma are challenging to assess routinely and easily⁴. Given the high prevalence of asthma, there is high potential impact of improved diagnostic tools on reducing morbidity and mortality from asthma. Biomarkers could improve the identification of mild/moderate asthma so that appropriate management can be pursued.

National and international guidelines recommend that the diagnosis of asthma should be based on a history of typical symptoms and objective findings of variable expiratory airflow limitation^(6, 7). However, obtaining such objective findings is challenging given currently available tools. Pulmonary function tests (PFTs) require equipment, expertise, and experience to execute well^(8, 9). Many individuals have difficulty with PFTs (e.g., spirometry) because they require coordinated breaths into a device. Results are unreliable if the procedure is done with poor technique⁸. Large epidemiologic studies of both children and adults substantiate that despite guidelines recommending objective tests such as PFTs to assess possible asthma, PFTs are not done in over half of patients suspected of having asthma⁸. Induced sputum and exhaled nitric oxide have been explored as asthma biomarkers, but their implementation requires technical expertise and does not yield better clinical results than physician-guided management alone¹⁰. Given the above, the reality is that most asthma is still clinically diagnosed and managed in children and adults based on self-report^(8, 9). This is suboptimal for mild/moderate asthma given its waxing/waning nature, and because self-reported symptoms and medication use are biased¹¹. There is need to improve asthma diagnosis, and an accurate biomarker of mild/moderate asthma could help meet that need. The ideal biomarker of mild/moderate asthma would be (1) obtainable noninvasively, (2) obtainable quickly, (3) interpretable without substantial expertise or infrastructure.

A nasal biomarker of asthma is of high interest given the accessibility of the nose and shared airway biology between the upper and lower respiratory tracts^(12, 13, 14, 15). The easily accessible nasal passages are directly connected to the lungs and exposed to common environmental and microbial factors. An accurate nasal biomarker of asthma that could be quickly obtained by a simple nasal brush could improve asthma diagnosis in adult and pediatric populations.

An asthma-specific gene panel has high potential to be used as a non-invasive biomarker to aid in asthma diagnosis, as it can be quickly obtained by simple nasal brush, does not require machinery for collection, and is easily interpreted. As discussed herein, objective findings of asthma are often not obtainable. Patients with mild/moderate asthma may not be asymptomatic at the time of the clinical encounter, so they may have no detectable wheezing or cough on exam. In many cases, then, a clinician may diagnose asthma on the basis of history alone, and this contributes to the under-diagnosis and misclassification of asthma. Studies have shown that patients with active asthma under-perceive their symptoms and do not tell their primary care physician. An objective diagnostic tool that is easy and quick to obtain and interpret with minimal effort required by the provider and patient could improve asthma diagnosis so that appropriate management can be pursued. A nasal brush-based asthma gene panel meets these biomarker criteria and capitalizes on the common biology of the upper and lower airway, a concept supported by clinical practice and previous findings.

In finding nasal biomarkers of mild/moderate asthma (FIG. 1), the inventors used next-generation RNA sequencing and data analysis to comprehensively profile nasal epithelial gene expression from nasal brushings collected from a well-characterized cohort of subjects with mild/moderate asthma and non-asthmatic controls. These technologies have contributed to advances in several areas of biomedicine, such as disease biomarker identification¹⁶, personalized medicine and treatment¹⁷. Specifically, the inventors used RNA sequencing to comprehensively profile gene expression from nasal brushings collected from subjects with mild to moderate asthma and controls. Using a robust machine learning-based pipeline comprised of feature selection¹⁸, classification¹⁹ and statistical analyses of performance²⁰, the inventors identified a gene panel with 275 unique genes, and subsets specific for different classification analyses, that can accurately differentiate subjects with and without mild-moderate asthma. This asthma gene panel was validated on eight test sets of independent subjects with asthma and other respiratory conditions, finding that it performed with high accuracy, sensitivity, and specificity. As used herein, the term “asthma gene panel” refers to these 275 genes collectively (see Table 4 for the list of genes and subsets). A subset of the asthma gene panel, the LR-RFE & Logistic asthma gene panel, was tested on three additional, independent cohorts of asthmatics and controls, and this panel consistently performed with accuracy. Further testing of the LR-RFE & Logistic asthma gene panel on five cohorts with non-asthma respiratory diseases validated the specificity of this nasal biomarker panel to asthma. The asthma gene panel currently identified through machine learning can be applied as a nasal brush-based biomarker tool for the clinical diagnosis of asthma, including mild/moderate asthma, and for distinguishing asthma from other respiratory disorders. Both diagnosis and differentiation with the invented methods enable the accurate diagnosis and treatment of asthma, including mild to moderate asthma, in the patient.

What is needed, therefore, is a noninvasive, quick and simple method for reliably diagnosing and/or classifying asthma, including but not limited to mild to moderate asthma, as well as distinguishing asthma from other respiratory disorders, and subsequently treating the patient appropriately. It is to such a method that embodiments of the present invention are primarily directed.

BRIEF SUMMARY OF THE INVENTION

As specified in the Background Section, there is a great need in the art to identify technologies for reliable, consistent, simple and non-invasive diagnosis of asthma, including but not limited to mild to moderate asthma, and use this understanding to develop novel diagnostic methods. The present invention satisfies this and other needs. Embodiments of the present invention relate generally to methods for diagnosis, classification and monitoring of asthma, including but not limited to mild to moderate asthma, and its differentiation from other respiratory disorders by determining the expression profiles of asthma-specific genes in nasal swab/scraping/brushing/wash/sponge samples.

In one aspect, the present invention provides a method for diagnosing asthma in a subject, comprising the steps of:

a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject;

b) performing classification analysis on the gene counts obtained from the gene expression profile(s);

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and

d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.

In another aspect, the present invention provides a method for detection of asthma in a subject, comprising the steps of:

a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject;

b) performing classification analysis on the gene counts obtained from the gene expression profile(s);

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and

d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.

In one aspect, the present invention provides a method for differentially diagnosing asthma from other respiratory disorders in a subject, comprising the steps of:

a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject;

b) performing classification analysis on the gene counts obtained from the gene expression profile(s);

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and

d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.

In one aspect, the present invention provides a method for classifying a subject as having asthma or not having asthma, comprising the steps of:

a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject;

b) performing classification analysis on the gene counts obtained from the gene expression profile(s);

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and

d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.

In another aspect, the present invention provides a method for monitoring asthma in a subject, comprising the steps of:

a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject;

b) performing classification analysis on the gene counts obtained from the gene expression profile(s);

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and

d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.

In one aspect, the present invention provides a method for selecting a subject for a clinical trial for asthma therapeutic compositions and/or methods, comprising the steps of:

a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject;

b) performing classification analysis on the gene counts obtained from the gene expression profile(s);

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and

d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.

In one aspect, the present invention provides a method for treating asthma in a subject, comprising the steps of:

a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject;

b) performing classification analysis on the gene counts obtained from the gene expression profile(s);

c) comparing the probability output obtained from the classification analysis to the optimal classification threshold;

d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold; and

e) utilizing appropriate therapeutic compositions and/or methods if the subject has asthma.

In one aspect, the present invention provides a kit for diagnosing and/or detecting asthma in a subject, said kit comprising probes directed towards one or more of the genes in the asthma gene panel, as described in more detail herein, wherein the probes can be used to determine the expression levels of one or more of the genes in the asthma gene panel. The kit can also comprise (i) a detection means and/or (ii) an amplification means. The kit may further optionally include control probe sets for detection of control RNA in order to provide a control level as described herein.

In another aspect, the present invention provides a kit for diagnosing and/or detecting asthma in a subject, said kit comprising pairs of oligonucleotides directed towards one or more of the genes in the asthma gene panel, as described in more detail herein, wherein the pairs of oligonucleotides can be used to determine the expression levels of one or more of the genes in the asthma gene panel. The kit can also comprise (i) a detection means and/or (ii) an amplification means. The kit may further optionally include control primer/oligonucleotide sets for detection of control RNA in order to provide a control level as described herein.

In any of the above embodiments, step (a) further comprises the steps of (i) brushing, swabbing, scraping, washing or sponging the patient's nose, (ii) obtaining and appropriately preserving the nasal brushing/swab/scraping/wash/sponge sample, and (iii) assaying the gene expression profile of the cells and tissue contained in the sample, whether by isolating RNA as described herein or by use of a RNA profiling system that does not require a separate isolation step (such as, for example and not limitation, nanoString).

In any of the above embodiments, steps (b) and/or (c) and/or (d) are performed by a computer.

In any of the above embodiments, the classification analysis can comprise the Logistic Regression-Recursive Feature Elimination (LR-RFE) algorithm in combination with the Logistic algorithm as described in more detail below, with the gene expression profiles analyzed by this LR-RFE & Logistic model being the expression profiles of the genes in the LR-RFE & Logistic asthma gene panel. In this embodiment, the optimal classification threshold is about 0.76.

In any of the above embodiments, the classification analysis can alternatively comprise the LR-RFE & SVM-Linear combination model as described in more detail below, with the gene expression profiles analyzed by this model being the expression profiles of the genes in the LR-RFE & SVM-Linear asthma gene panel. The optimal classification threshold for this model is about 0.52.

In any of the above embodiments, the classification analysis can alternatively comprise the SVM-RFE & SVM-Linear model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the SVM-RFE & SVM-Linear asthma gene panel, and the optimal classification threshold for this model is about 0.64.

In any of the above embodiments, the classification analysis can alternatively comprise the SVM-RFE & Logistic model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the SVM-RFE & Logistic asthma gene panel, and the optimal classification threshold for this model is about 0.69.

In any of the above embodiments, the classification analysis can alternatively comprise the LR-RFE & AdaBoost model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the LR-RFE & AdaBoost asthma gene panel, and the optimal classification threshold for this model is about 0.49.

In any of the above embodiments, the classification analysis can alternatively comprise the LR-RFE & RandomForest model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the LR-RFE & RandomForest asthma gene panel, and the optimal classification threshold for this model is about 0.60.

In any of the above embodiments, the classification analysis can alternatively comprise the SVM-RFE & RandomForest model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the SVM-RFE & RandomForest asthma gene panel, and the optimal classification threshold for this model is about 0.50.

In any of the above embodiments, the classification analysis can alternatively comprise the SVM-RFE & AdaBoost model as described in more detail below, the gene expression profiles analyzed by this model being the expression profiles of the genes in the SVM-RFE & AdaBoost asthma gene panel, and the optimal classification threshold for this model is about 0.55.

In any of the above embodiments, the patient is a mammal. In any of the above embodiments, the patient is a human.

These and other objects, features and advantages of the present invention will become more apparent upon reading the following specification in conjunction with the accompanying description, claims and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying Figures, which are incorporated in and constitute a part of this specification, illustrate several aspects described below.

FIG. 1 depicts the study flow for the identification of a nasal biomarker of asthma by machine learning analysis of next-generation transcriptomic data. Subjects with mild/moderate asthma and nonasthmatic controls were recruited for phenotyping, nasal brushing, and RNA sequencing of nasal epithelium. The RNAseq data generated were then a priori split into a development and test set. The development set was used for differential expression analysis and machine learning (involving feature selection, classification, and statistical analyses of classification performance) to identify an asthma gene panel that can accurately classify asthma from no asthma. Several classification models, including LR-RFE & Logistic, LR-RFE & SVM-Linear, SVM-RFE & Logistic, SVM-RFE & SVM-Linear, LR-RFE & AdaBoost, LR-RFE & RandomForest, SVM-RFE & RandomForest, and SVM-RFE & AdaBoost, were used to identify member genes of the asthma gene panel. The asthma gene panel identified was then tested on eight validation test sets, including (1) the RNAseq test set of subjects with and without asthma, (2) two test sets of subjects with and without asthma with nasal gene expression profiled by microarray, and (3) five test sets of subjects with non-asthma respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, and smoking) and nasal gene expression profiled by microarray. The strong precision and recall of the asthma gene panel across all test sets, reflected in the combined strong F-measure values, support its high potential to translate into a nasal brush-based biomarker for asthma diagnosis.

FIG. 2 shows the receiver operating characteristic (ROC) curve of the predictions generated by applying the asthma gene panel to the samples in the RNAseq test set of independent subjects (n=40). The ROC curve for a random model is shown for reference. The curve and its corresponding AUC score show that the panel performs well for both asthma and no asthma (control) samples in this test set.

FIG. 3 shows the validation of the asthma gene panel on test sets of independent subjects with asthma. Performance of the asthma panel in classifying asthma and no asthma in terms of Fmeasure, a conservative mean of precision and sensitivity²⁸. F-measure ranges from 0 to 1, with higher values indicating superior classification performance. The panel was applied to an RNAseq test set of independent subjects with and without asthma, and two external microarray data sets from subjects with and without asthma (Asthma1 and Asthma2).

FIG. 4 shows the comparative performance in the RNAseq test set of the LR-RFE & Logistic asthma gene panel and other classification models processed through the inventors' machine learning pipeline. Performances of the LR-RFE & Logistic asthma gene panel and other classification models in classifying asthma (left panel) and no asthma (right panel) are shown in terms of F-measure, with individual measures shown in the bars. The number of genes in each model is shown in parentheses within the bars. The LR-RFE & Logistic classification model is listed first, followed by the other classification models. These other classification models were combinations of two feature selection algorithms (LR-RFE and SVM-RFE) and four global classification algorithms (Logistic Regression, SVM-Linear, AdaBoost and Random Forest). For context, alternative classification models are also shown and include: (1) a model derived from an alternative, single-step classification approach (sparse classification model learned using the L1-Logistic regression algorithm), and (2) models substituting feature selection with each of the following preselected gene sets—all genes, all differentially expressed genes, and known asthma genes²⁹—with their respective best performing global classification algorithms. These results show the performance of the LR-RFE & Logistic asthma gene panel compared to all other models, in terms of classification performance and/or model parsimony (number of genes included). LR=Logistic Regression. SVM=Support Vector Machine. RFE=Recursive Feature Elimination. RF=Random Forest.

FIG. 5 shows the validation of the LR-RFE & Logistic asthma gene panel on test sets of independent subjects with non-asthma respiratory conditions. Performance statistics of the panel when applied to external microarray-generated data sets of nasal gene expression derived from case/control cohorts with non-asthma respiratory conditions. The LR-RFE & Logistic panel had a low to zero rate of misclassifying other respiratory conditions as asthma, supporting that the LR-RFE & Logistic panel is specific to asthma and would not misclassify other respiratory conditions as asthma.

FIG. 6 shows a heatmap showing expression profiles of the 90 gene members of the LR-RFE & Logistic asthma gene panel. Columns shaded dark grey (right-hand side) at the top denote asthma samples, while samples from subjects without asthma are denoted by columns shaded light grey (left-hand side). 22 and 24 of these genes were over- and under-expressed in asthma samples (DESeq2 FDR≤0.05), denoted by medium grey (uppermost group) and dark grey (middle group) groups of rows, respectively. The four genes in this set that have been previously associated with asthma²⁹ are C3, DEFB1, CYFIP2, and GSTT1. The LR-RFE & Logistic panel's inclusion of genes not previously known to be associated with asthma as well as genes not differentially expressed in asthma (light grey lowermost group of rows) demonstrates the ability of the inventors' machine learning methodology to move beyond traditional analyses of differential expression and current domain knowledge.

FIG. 7 shows variancePartition analysis of the RNAseq development set. Gene expression variation across RNA samples due to age, race, and sex was assessed by variancePartition and found to be minimal.

FIG. 8 shows a visual description of the machine learning pipeline used to select predictive features (genes) and develop classification models based on them from the RNAseq development set. By considering 100 splits of the development set into training and holdout sets (dotted box), many such models were evaluated for classification performance and then compared statistically using Friedman and Nemenyi tests. From this comparison, a highly precise combination of predictive genes and outer classification algorithms with good recall was determined, namely the LR-RFE & Logistic (Regression) model. This combination was in turn executed on the development set to train the LR-RFE & Logistic asthma gene panel. This LR-RFE & Logistic model was applied to several independent RNAseq and external microarray-derived cohorts with asthma and other respiratory conditions for final evaluation.

FIG. 9 shows a visual description of the feature (gene) selection component of the invented machine learning pipeline. Given a training set, this component used a 5×5 nested (outer and inner) cross-validation (CV) setup to select sets of predictive features (genes). The inner CV round was used to determine the optimal number of features to be selected, and the outer one was used to select the set of predictive genes based on this number, thus reducing the cumulative effect of these potential sources of overfitting. The selection of features itself was performed using the Recursive Feature Elimination (RFE) algorithm in combination with wrapper Logistic Regression and SVM with Linear kernel classification algorithms.

FIG. 10A-10B shows Critical Difference plots demonstrating the statistical comparison of the performance of 100 asthma classification models obtained by various combinations of feature selection and outer classification algorithms. To emphasize the need for parsimony (small feature/gene sets) in these models, an adapted performance measure defined as the F-measure for each model divided by the number of genes in that model is used for this comparison. The Friedman followed by Nemenyi tests were used to statistically compare these adapted measures and obtain the p-values constituting the above plot. Each combination is represented individually by vertical+horizontal lines on the (10A) asthma and (10B) no asthma classes constituting the RNASeq development set. Combinations with improving performance are laid out from the left to right in terms of the average rank obtained by each of their 100 models, and the combinations connected by thick black lines perform statistically equivalently. The LR-RFE & Logistic model, which identified 90 genes (listed in Table 4 below) is a highly performing combination since, on average, it achieves good performance with the fewest selected genes. Other models that performed well, along with the identified genes, are listed in Table 4 below and discussed in more detail below. Across all eight of the models, 275 unique genes were identified as listed in Table 4.

FIG. 11 shows evaluation measures for classification models. The relationships between F-measure, sensitivity, precision, recall, positive predictive value, and negative predictive value are summarized. F-measure, which is a harmonic (conservative) mean of precision and recall that is computed separately for each class, provides a more comprehensive and reliable assessment of model performance when classes are imbalanced, as is frequently the case in biomedical scenarios.

FIG. 12 shows the performance of permutation-based random classification models in test sets of independent subjects with asthma and controls. To determine the extent to which the classification performance of the LR-RFE & Logistic asthma gene panel could have been due to chance, 100 permutation-based random models were obtained by randomly permuting the labels of the samples in the development set and executing each of the feature selection-global classification combinations on these randomized data sets in the same way as described above for the real development set. These random models were then applied to each of the asthma test sets considered in our study, and their performances were also evaluated in terms of the F-measure.

FIG. 13 shows the performance of permutation-based random classification models in test sets of independent subjects with non-asthma respiratory conditions and controls. 100 permutation-based random models were obtained by randomly permuting the labels of the samples in the development set and executing each of the feature selection-global classification combinations on these randomized data sets in the same way as described above for the real development set. These random models were then applied to these test sets, and their performances were also evaluated in terms of the F-measure.

FIG. 14 shows the distribution of DESeq2 FDR values of differentially expressed genes in the LR-RFE & Logistic asthma gene panel (dark grey bars) vs. other genes in the RNAseq development set (white bars), with overlaps between the bars shown in light grey. The Y-axis shows the probability of a gene having a −log 10(FDR) value in the corresponding bin. This plot shows that the genes in the LR-RFE & Logistic asthma panel were likely to be more differentially expressed, i.e., higher −log 10(FDR) or lower differential expression FDRs, than other genes in the development set.

DETAILED DESCRIPTION OF THE INVENTION

As specified in the Background Section, there is a great need in the art to identify technologies for reliable, consistent, simple and non-invasive diagnosis of asthma, including but not limited to mild to moderate asthma and use this understanding to develop novel diagnostic methods. The present invention satisfies this and other needs. Embodiments of the present invention relate generally to methods for diagnosis, classification and monitoring of asthma, including but not limited to mild to moderate asthma, and its differentiation from other respiratory disorders by determining the expression profiles of asthma-specific genes in nasal swab/scraping/brushing samples.

To facilitate an understanding of the principles and features of the various embodiments of the invention, various illustrative embodiments are explained below. Although exemplary embodiments of the invention are explained in detail, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the invention is limited in its scope to the details of construction and arrangement of components set forth in the following description or examples. The invention is capable of other embodiments and of being practiced or carried out in various ways. Also, in describing the exemplary embodiments, specific terminology will be resorted to for the sake of clarity.

It must also be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural references unless the context clearly dictates otherwise. For example, reference to a component is intended also to include composition of a plurality of components. References to a composition containing “a” constituent is intended to include other constituents in addition to the one named. In other words, the terms “a,” “an,” and “the” do not denote a limitation of quantity, but rather denote the presence of “at least one” of the referenced item.

Also, in describing the exemplary embodiments, terminology will be resorted to for the sake of clarity. It is intended that each term contemplates its broadest meaning as understood by those skilled in the art and includes all technical equivalents which operate in a similar manner to accomplish a similar purpose.

Ranges may be expressed herein as from “about” or “approximately” or “substantially” one particular value and/or to “about” or “approximately” or “substantially” another particular value. When such a range is expressed, other exemplary embodiments include from the one particular value and/or to the other particular value. Further, the term “about” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within an acceptable standard deviation, per the practice in the art. Alternatively, “about” can mean a range of up to ±20%, preferably up to ±10%, more preferably up to ±5%, and more preferably still up to ±1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 2-fold, of a value. Where particular values are described in the application and claims, unless otherwise stated, the term “about” is implicit and in this context means within an acceptable error range for the particular value.

By “comprising” or “containing” or “including” is meant that at least the named compound, element, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, materials, particles, method steps, even if the other such compounds, material, particles, method steps have the same function as what is named.

Throughout this description, various components may be identified having specific values or parameters, however, these items are provided as exemplary embodiments. Indeed, the exemplary embodiments do not limit the various aspects and concepts of the present invention as many comparable parameters, sizes, ranges, and/or values may be implemented. The terms “first,” “second,” and the like, “primary,” “secondary,” and the like, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

It is noted that terms like “specifically,” “preferably,” “typically,” “generally,” and “often” are not utilized herein to limit the scope of the claimed invention or to imply that certain features are critical, essential, or even important to the structure or function of the claimed invention. Rather, these terms are merely intended to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the present invention. It is also noted that terms like “substantially” and “about” are utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation.

The dimensions and values disclosed herein are not to be understood as being strictly limited to the exact numerical values recited. Instead, unless otherwise specified, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as “50 mm” is intended to mean “about 50 mm.”

It is also to be understood that the mention of one or more method steps does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Similarly, it is also to be understood that the mention of one or more components in a composition does not preclude the presence of additional components than those expressly identified.

As used herein, the term “subject” or “patient” refers to mammals and includes, without limitation, human and veterinary animals. In a preferred embodiment, the subject is human.

In the context of the present invention insofar as it relates to asthma, the terms “treat”, “treatment”, and the like mean to relieve or alleviate at least one symptom associated with such condition, or to slow or reverse the progression of such condition. Within the meaning of the present invention, the term “treat” also denotes to arrest, delay the onset (i.e., the period prior to clinical manifestation of a disease) and/or reduce the risk of developing or worsening a disease. The terms “treat”, “treatment”, and the like regarding a state, disorder or condition may also include (1) preventing or delaying the appearance of at least one clinical or sub-clinical symptom of the state, disorder or condition developing in a subject that may be afflicted with or predisposed to the state, disorder or condition but does not yet experience or display clinical or subclinical symptoms of the state, disorder or condition; or (2) inhibiting the state, disorder or condition, i.e., arresting, reducing or delaying the development of the disease or a relapse thereof (in case of maintenance treatment) or at least one clinical or sub-clinical symptom thereof; or (3) relieving the disease, i.e., causing regression of the state, disorder or condition or at least one of its clinical or sub-clinical symptoms.

The term “a control level” as used herein encompasses predetermined standards (e.g., a published value in a reference) as well as levels determined experimentally in similarly processed samples from control subjects (e.g., BMI-, age-, and gender-matched subjects without asthma as determined by standard examination and diagnostic methods). The control level is included in the classification analyses as described herein.

RNA can be extracted from the collected tissue and/or cells (e.g., from nasal epithelial cells obtained from a nasal brushing, scraping, wash, sponge or swab) by any known method. For example, RNA may be purified from cells using a variety of standard procedures as described, for example, in RNA Methodologies, A Laboratory Guide for Isolation and Characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press. In addition, various commercial products are available for RNA isolation. As would be understood by those skilled in the art, total RNA or polyA+RNA may be used for preparing gene expression profiles.

The expression levels (or expression profile) can be then determined using any of various techniques known in the art and described in detail elsewhere. Such methods generally include, for example and not limitation, polymerase-based assays such as RT-PCR (e.g., TAQMAN), hybridization-based assays such as DNA microarray analysis, flap-endonuclease-based assays (e.g., INVADER), direct mRNA capture (QUANTIGENE or HYBRID CAPTURE (Digene)), RNA sequencing (e.g., Illumina RNA sequencing platforms), and by the nanoString platform. See, for example, US 2010/0190173 for descriptions of representative methods that can be used to determine expression levels.

As used herein, the term “gene” refers to a DNA sequence expressed in a sample as an RNA transcript.

As used herein, “differentially expressed” or “differential expression” means that the level or abundance of an RNA transcripts (or abundance of an RNA population sharing a common target sequence (e.g., splice variant RNAs)) is higher or lower by at least a certain value in a test sample as compared to a control level.

As used herein, the term “asthma gene panel” refers to the unique set of 275 genes identified by all of the models and listed in Table 4 as the unique set of genes. Preferred subsets of the asthma gene panel that may be analyzed by different classifiers are also described in Table 4. Specifically, as used herein, the term “LR-RFE & Logistic asthma gene panel” refers to those 90 genes identified by the LR-RFE & Logistic models. The term “LR-RFE & SVM-Linear asthma gene panel” refers to those 90 genes identified by the LR-RFE & SVM-Linear models. The term “SVM-RFE & SVM-Linear asthma gene panel” refers to those 119 genes identified by the SVM-RFE & SVM-Linear models. The term “SVM-RFE & Logistic asthma gene panel” refers to those 119 genes identified by the SVM-RFE & Logistic models. The term “LR-RFE & AdaBoost asthma gene panel” refers to those 90 genes identified by the LR-RFE & AdaBoost models. The term “LR-RFE & RandomForest asthma gene panel” refers to those 90 genes identified by the LR-RFE & RandomForest models. The term “SVM-RFE & RandomForest asthma gene panel” refers to those 123 genes identified by the SVM-RFE & RandomForest models. The term “SVM-RFE & AdaBoost asthma gene panel” refers to those 212 genes identified by the SVM-RFE & AdaBoost models.

In various embodiments disclosed herein, the expression levels of different combinations of genes can be used to glean different information. For example, increased expression levels of certain genes such as C3 in an individual as compared to a control are associated with a diagnosis of mild/moderate asthma. Decreased expression levels of other genes such as DEFB1 in an individual as compared to a control are associated with a diagnosis of mild/moderate asthma. Expression of ORMDL3 in an individual as compared to a control is associated with a differential diagnosis of mild/moderate asthma relative to other respiratory disorders such as, for example and not limitation, rhinitis, respiratory infection, and cystic fibrosis.

In various embodiments, RNA expression profiling systems are utilized to quantify the gene expression profiles from the patient's nasal brushing/swab/scraping/washing/sponge, such as for example and not limitation, the nanoString profiling system. The output from such systems will provide a count of genes in the asthma gene panel, and such output is analyzed in an automated manner, such as by a computer, via the classifier and classification threshold as described herein. The results obtained from the classifier enable a clinician to diagnose the patient as having asthma or not.

After determining and analyzing the expression levels of the appropriate combination of genes in a patient's nasal brushing/swab/scraping/washing/sponge, the patient can be classified as having asthma or not having asthma. The classification may be determined computationally based upon known methods as described herein. Particularly preferred computational methods include the classifiers and optimal classification thresholds as described herein. The result of the computation may be displayed on a computer screen or presented in a tangible form, for example, as a probability (e.g., from 0 to 100%) of the patient having asthma and/or a certain severity of asthma. The report will aid a physician in diagnosis or treatment of the patient. For example, in certain embodiments, the patient's expression levels will be diagnostic of asthma or enable a differential diagnosis of asthma from other respiratory disorders such as rhinitis, irritation resulting from smoking, respiratory infection and cystic fibrosis, and the patient will subsequently be treated as appropriate. In other embodiments, the patient's expression levels of the appropriate combination of genes will not support a diagnosis of asthma, thereby allowing the physician to exclude asthma and/or mild to moderate asthma as a diagnosis. In some embodiments, the patient may be selected to participate in clinical trials involving treatment of asthma and/or related conditions based on the patient's gene expression profile.

In some embodiments, the classifier used is the LR-RFE & Logistic model, the gene expression profiles analyzed are the expression profiles of the genes in the LR-RFE & Logistic asthma gene panel, and the optimal classification threshold for this model is about 0.76.

In other embodiments, the classifier used is the LR-RFE & SVM-Linear model, the gene expression profiles analyzed are the expression profiles of the genes in the LR-RFE & SVM-Linear asthma gene panel, and the optimal classification threshold for this model is about 0.52.

In other embodiments, the classifier used is the SVM-RFE & SVM-Linear model, the gene expression profiles analyzed are the expression profiles of the genes in the SVM-RFE & SVM-Linear asthma gene panel, and the optimal classification threshold for this model is about 0.64.

In other embodiments, the classifier used is the SVM-RFE & Logistic model, the gene expression profiles analyzed are the expression profiles of the genes in the SVM-RFE & Logistic asthma gene panel, and the optimal classification threshold for this model is about 0.69.

In other embodiments, the classifier used is the LR-RFE & AdaBoost model, the gene expression profiles analyzed are the expression profiles of the genes in the LR-RFE & AdaBoost asthma gene panel, and the optimal classification threshold for this model is about 0.49.

In other embodiments, the classifier used is the LR-RFE & RandomForest model, the gene expression profiles analyzed are the expression profiles of the genes in the LR-RFE & RandomForest asthma gene panel, and the optimal classification threshold for this model is about 0.60.

In other embodiments, the classifier used is the SVM-RFE & RandomForest model, the gene expression profiles analyzed are the expression profiles of the genes in the SVM-RFE & RandomForest asthma gene panel, and the optimal classification threshold for this model is about 0.50.

In other embodiments, the classifier used is the SVM-RFE & AdaBoost model, the gene expression profiles analyzed are the expression profiles of the genes in the SVM-RFE & AdaBoost asthma gene panel, and the optimal classification threshold for this model is about 0.55.

In some embodiments, RNAs are purified prior to gene expression profile analysis. RNAs can be isolated and purified from nasal brushing/swab/scraping/wash/sponge by various methods, including the use of commercial kits (e.g., Qiagen RNeasy Mini Kit as described in Example 1 below). In some embodiments, RNA degradation in brushing/swab/scraping/wash/sponge samples and/or during RNA purification is reduced or eliminated. Useful methods for storing nasal brushing/swab/scraping/wash/sponge samples include, without limitation, use of RNALater as described herein. Useful methods for reducing or eliminating RNA degradation include, without limitation, adding RNase inhibitors (e.g., RNasin Plus [Promega], SUPERase-In [ABI], etc.), use of guanidine chloride, guanidine isothiocyanate, N-lauroylsarcosine, sodium dodecylsulphate (SDS), or a combination thereof. Reducing RNA degradation in nasal brushing/swab/scraping/wash/sponge samples is particularly important when sample storage and transportation is required prior to RNA purification.

In other embodiments, RNA is not purified prior to gene expression profile analysis. In such embodiments, RNA expression profiling platforms that can directly assay tissue and cells without a separate RNA isolation step are utilized (for example and not limitation, the nanoString system).

Examples of useful methods for measuring RNA level in nasal epithelial cells contained in nasal brushing/swab/scraping/wash/sponge include hybridization with selective probes (e.g., using Northern blotting, bead-based flow-cytometry, oligonucleotide microchip [microarray], or solution hybridization assays), polymerase chain reaction (PCR)-based detection (e.g., stem-loop reverse transcription-polymerase chain reaction [RT-PCR], quantitative RT-PCR based array method [qPCR-array]), direct sequencing, such as for example and not limitation, by RNA sequencing technologies (e.g., Illumina HiSeq 2500 platform, Helicos small RNA sequencing, miRNA BeadArray (Illumina), Roche 454 (FLX-Titanium), and ABI SOLiD), and the nanoString system. For review of additional applicable techniques see, e.g., Chen et al., BMC Genomics, 2009, 10:407; Kong et al., J Cell Physiol. 2009; 218:22-25.

In conjunction with the above diagnostic and screening methods, the present invention provides various kits comprising one or more primer and/or probe sets specific for the detection of target RNA. Such kits can further include primer and/or probe sets specific for the detection of other RNA that can aid in diagnosing, differentiating, and/or classifying asthma. In some embodiments, such kits can contain nucleic acid oligonucleotides for determining the level of expression of a particular combination of genes in a patient's nasal brushing/swab/scraping/wash/sponge sample. The kit may include one or more oligonucleotides that are complementary to one or more transcripts identified herein as being associated with asthma, and also may include oligonucleotides related to necessary or meaningful assay controls. A kit for evaluating an individual for asthma may include pairs of oligonucleotides (e.g., 4, 6, 8, 10, 12, 14 or more oligonucleotides). The oligonucleotides may be designed to detect expression levels in accordance with any assay format, including but not limited to those described herein. The kit may further optionally include control primer and/or probe sets for detection of control RNA in order to provide a control level as described herein.

A kit of the invention can also provide reagents for primer extension and amplification reactions. For example, in some embodiments, the kit may further include one or more of the following components: a reverse transcriptase enzyme, a DNA polymerase enzyme (such as, e.g., a thermostable DNA polymerase), a polymerase chain reaction buffer, a reverse transcription buffer, and deoxynucleoside triphosphates (dNTPs). Alternatively (or in addition), a kit can include reagents for performing a hybridization assay. The detecting agents can include nucleotide analogs and/or a labeling moiety, e.g., directly detectable moiety such as a fluorophore (fluorochrome) or a radioactive isotope, or indirectly detectable moiety, such as a member of a binding pair, such as biotin, or an enzyme capable of catalyzing a non-soluble colorimetric or luminometric reaction. In addition, the kit may further include at least one container containing reagents for detection of electrophoresed nucleic acids. Such reagents include those which directly detect nucleic acids, such as fluorescent intercalating agent or silver staining reagents, or those reagents directed at detecting labeled nucleic acids, such as, but not limited to, ECL reagents. A kit can further include RNA isolation or purification means as well as positive and negative controls. A kit can also include a notice associated therewith in a form prescribed by a governmental agency regulating the manufacture, use or sale of diagnostic kits. Detailed instructions for use, storage and trouble-shooting may also be provided with the kit. A kit can also be optionally provided in a suitable housing that is preferably useful for robotic handling in a high throughput setting.

The components of the kit may be provided as dried powder(s). When reagents and/or components are provided as a dry powder, the powder can be reconstituted by the addition of a suitable solvent. It is envisioned that the solvent may also be provided in another container. The container will generally include at least one vial, test tube, flask, bottle, syringe, and/or other container means, into which the solvent is placed, optionally aliquoted. The kits may also comprise a second container means for containing a sterile, pharmaceutically acceptable buffer and/or other solvent.

Where there is more than one component in the kit, the kit also will generally contain a second, third, or other additional container into which the additional components may be separately placed. However, various combinations of components may be comprised in a container.

Such kits may also include components that preserve or maintain DNA or RNA, such as reagents that protect against nucleic acid degradation. Such components may be nuclease or RNase-free or protect against RNases, for example. Any of the compositions or reagents described herein may be components in a kit.

In accordance with the present invention there may be employed conventional molecular biology, microbiology, and recombinant DNA techniques within the skill of the art. Such techniques are explained fully in the literature. See, e.g., Sambrook, Fritsch & Maniatis, Molecular Cloning: A Laboratory Manual, Second Edition (1989) Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (herein “Sambrook et al., 1989”); DNA Cloning. A Practical Approach, Volumes I and II (D. N. Glover ed. 1985); Oligonucleotide Synthesis (M. J. Gait ed. 1984); Nucleic Acid Hybridization (B. D. Hames & S. J. Higgins eds. (1985); Transcription and Translation (B. D. Hames & S. J. Higgins, eds. (1984); Animal Cell Culture (R. I. Freshney, ed. (1986); Immobilized Cells and Enzymes (IRL Press, (1986); B. Perbal, A Practical Guide To Molecular Cloning (1984); F. M. Ausubel et al. (eds.), Current Protocols in Molecular Biology, John Wiley & Sons, Inc. (1994); among others.

EXAMPLES

The present invention is also described and demonstrated by way of the following examples. However, the use of these and other examples anywhere in the specification is illustrative only and in no way limits the scope and meaning of the invention or of any exemplified term. Likewise, the invention is not limited to any particular preferred embodiments described here. Indeed, many modifications and variations of the invention may be apparent to those skilled in the art upon reading this specification, and such variations can be made without departing from the invention in spirit or in scope. The invention is therefore to be limited only by the terms of the appended claims along with the full scope of equivalents to which those claims are entitled.

Example 1. Development of the Nasal Biomarker Panel Materials and Methods

Experimental Design and Subjects

Subjects with mild/moderate asthma were a subset of participants of the Childhood Asthma Management Program (CAMP), a multicenter North American clinical trial of 1041 subjects that took place between 1991 and 2012^(21,22). Findings from the CAMP cohort have defined current practice and guidelines for asthma care and research²². Participating subjects had asthma defined by symptoms greater than or equal to 2 times per week, use of an inhaled bronchodilator at least twice weekly or use of daily medication for asthma, and increased airway responsiveness to methacholine (PC₂₀≤12.5 mg/ml). The subset of subjects included in this study were CAMP participants who presented for a visit between July 2011 and June 2012 at Brigham and Women's Hospital, one of eight study centers for this multicenter study.

Subjects without asthma or “no asthma” were recruited during the same time period (2011-2012) by advertisement at Brigham & Women's Hospital. Selection criteria were no personal history of asthma, no family history of asthma in first degree relatives, and self-described non-Hispanic white ethnicity. The rationale for limiting participation to non-Hispanic white individuals was to allow for optimal comparison to 968 CAMP subjects of Caucasian background who participated in the CAMP Genetics Ancillary study, which was focused on this population.⁵⁵ Subjects underwent pre and post-bronchodilator spirometry according to ATS guidelines, and only those meeting selection criteria and without lung function abnormality or bronchodilator response were considered nonasthmatic or “no asthma”.

The institutional review boards of Brigham & Women's Hospital and the Icahn School of Medicine at Mount Sinai approved the study protocols.

Nasal Sample Collection and RNA Sequencing

A standard cytology brush was applied to the right nare of each subject and rotated three times with circumferential pressure for nasal epithelial cell collection. The brush was immediately placed in RNALater and then stored at 4° C. until RNA extraction. RNA extraction was performed with Qiagen RNeasy Mini Kit (Valencia, Calif.). Samples were assessed for yield and quality using the 2100 Bioanalyzer (Agilent Technologies, Santa Clara, Calif.) and Qubit (Thermo Fisher Scientific, Grand Island, N.Y.).

Of the 190 subjects who underwent nasal brushing (66 with mild/moderate asthma, 124 with no asthma), a random selection of 150 nasal brushes from subjects with asthma and nonasthmatic controls were a priori assigned as the development set, and the remaining 40 subjects were a priori assigned as the test set of independent subjects (for testing the classification model). To minimize potential bias due to batch effects, the inventors submitted all samples (training and test set samples) to the Mount Sinai Genomics Core for library preparation and RNA sequencing at the same time to allow for sequencing of all samples in a single run. Staff at the Mount Sinai Genomics Core were blinded to the assignment of samples as development or test set.

The sequencing library was prepared with the standard TruSeq RNA Sample Prep Kit v2 protocol (Illumina). The mRNA sequencing was performed on the Illumina HiSeq 2500 platform using 40-50 million 100 bp paired-end reads. The data were put through the inventors' standard mapping pipeline⁵⁶ (using Bowtie⁵⁷ and TopHat⁵⁸, and assembled into gene- and transcription-level summaries using Cufflinks⁵⁹). Mapped data were subjected to quality control with FastQC and RNA-SeQC.⁶⁰ Data were normalized separately for the development and test sets. Genes with fewer than 100 counts in at least half the samples were dropped to reduce the potentially adverse effects of noise. DESeq2²⁵ was used to normalize the data sets using its variance stabilizing transformation method.

VariancePartition Analysis of Potential Confounders

Given differences in age, race, and sex distributions between the asthma and “no asthma” classes, the inventors used variancePartition²⁴ to assess the degree to which these variables influenced gene expression. The total variance in gene expression was partitioned into the variance attributable to age, race, and sex using a linear mixed model implemented in variancePartition v1.0.0²⁴. Age (continuous variable) was modeled as a fixed effect while race and sex (categorical variables) were modeled as random effects. The results showed that age, race, and sex accounted for minimal contributions to total gene expression variance (FIG. 7).

Downstream Analyses were Therefore Performed with Unadjusted Gene Expression Data.

Differential gene expression and pathway enrichment analysis DESeq2²⁵ was used to identify differentially expressed genes in the development set. Genes with FDR≤0.05 were deemed differentially expressed, with fold change <1 implying under-expression and vice versa. Pathway enrichment analysis was performed using Gene SetEnrichment Analysis²⁶.

Statistical and Machine Learning Analyses of RNAseq Data Sets

To discover gene expression biomarkers that are capable of predicting the asthma status of a patient, the inventors used a rigorous machine learning pipeline in Python using the scikit-learn package⁶¹. This pipeline combined feature (gene) selection¹⁸, (outer) classification¹⁹ and statistical analyses of classification performance²⁰ to the development set (FIG. 8). The first two components, feature selection and classification, were applied to a training set constituted of 120 randomly selected samples from the development set (n=150) to learn classification models. These models were evaluated on the corresponding remaining 30 samples (holdout set). This process (feature selection and classification) was repeated 100 times on 100 random splits of the development set into training and holdout sets.

Feature (Gene) Selection:

Given a training set, a 5×5 nested (outer and inner) cross-validation (CV) setup²⁷ was used to select sets of predictive genes (FIG. 9). The inner CV round was used to determine the optimal number of genes to be selected, and the outer CV round was used to select the set of predictive genes based on this number, thus reducing the cumulative effect of these potential sources of overfitting.

The Recursive Feature Elimination (RFE) algorithm⁶² was executed on the inner CV training split to determine the optimal number of features. The use of RFE within this setting enabled the inventors to identify groups of features that are collectively, but not necessarily individually, predictive. This reflects the systems biology-based expectation that many genes, even ones with marginal effects, can play a role in classifying diseases/phenotypes (here asthma) in combination with other more strongly predictive genes⁶³. Specifically, the inventors used the L2-regularized Logistic Regression (LR or Logistic)⁶⁴ and SVM-Linear(kernel)⁶⁵ classification algorithms in conjunction with RFE (conjunctions henceforth referred to as LR-RFE and SVM-RFE respectively). For this, for a given inner CV training split, all the features (genes) were ranked using the absolute values of the weights assigned to them by an inner classification model, trained using the LR or SVM algorithm, over this split. Next, for each of the conjunctions, the set of top-k ranked features, with k starting with 11587 (all filtered genes) and being reduced by 10% in each iteration until k=1, was considered. The discriminative strength of feature sets consisting of the top k features as per this ranking was assessed by evaluating the performance of the LR or SVM classifier based on them over all the inner CV training-test splits. The optimal number of features to be selected was determined as the value of k that produces the best performance. Next, a ranking of features was derived from the outer CV training split using exactly the same procedure as applied to the inner CV training split. The optimal number of features determined above was selected from the top of this ranking to determine the optimal set of predictive features for this outer CV training split. Executing this process over all the five outer CV training splits created from the development set identified five such sets. Finally, the set of features (genes) that was common to all these sets (i.e., in their intersection/overlap) was selected as the predictive gene set for this training set. One such set was identified for LR-RFE and SVM-RFE respectively.

(Outer) Classification:

Once respective predictive gene sets had been selected using LR-RFE and SVM-RFE, four outer classification algorithms, namely L2-regularized Logistic Regression (LR or Logistic)⁶⁴, SVM-Linear⁶⁶, AdaBoost⁶⁶ and Random Forest (RF)⁶⁷, were used to learn intermediate classification models over the training set. These intermediate models were applied to the corresponding holdout set to generate probabilistic asthma predictions for the constituent samples. An optimal threshold for converting these probabilistic predictions into binary ones was then computed from the holdout set. This optimization resulted in the proposed classification models. This optimization resulted in proposed classification models.

To obtain a comprehensive view of the performance of these proposed models, the above two components were executed on 100 random training-holdout splits of the development set. To determine the best performing combination of feature selection and outer classification algorithms, a statistical analysis of the classification performance of all the models resulting from all the considered combinations was conducted using the Friedman followed by the Nemenyi test^(20,68) These tests, which account for multiple hypothesis testing, assessed the statistical significance of the relative difference of performance of the combinations in terms of their relative ranks across the 100 splits, and allow the ordering of the overall performance of each combination in terms of the significance of their pairwise comparison. This statistical comparison was a novel aspect of the present pipeline, as this task, generally referred to as “model selection,” is typically based on a single training-holdout split. Even if multiple such splits are employed, models are generally selected based on absolute performance scores, and not based on the statistical significance of performance comparisons, as was done in the present Examples.

Optimization for parsimony: For biomarker optimization, it is essential to consider parsimony (i.e., minimize number of features or genes for accurate classification) In these models, an adapted performance measure, defined as the absolute performance measure for each model divided by the number of genes in that model, was used for this statistical comparison. In terms of this measure, a model that does not obtain the best absolute performance measure among all models, but uses much fewer genes than the other, may be judged to be the best model. The result of this statistical analysis, visualized as a Critical Difference plot²⁸ (FIG. 10A-10B), enabled identification of the good-performing combination of feature selection and outer classification methods in terms of both performance and parsimony.

Final Model Development and Evaluation:

The final step in the pipeline was to determine the representative model from the 100 iterations of the most statistically superior combination of feature selection and classification method identified from the above steps. In case of ties among the models of the best performing combination, the gene set that produced the best asthma classification F-measure (FIG. 11) across all four global classification algorithms was chosen as the gene set constituting the representative model for that combination. The result of this process was the asthma gene panel-based model that consisted of this representative gene set for each of eight models, a global classification algorithm and each model's optimized threshold for classifying samples with and without asthma. This optimized threshold was determined for this model as the one that produced the highest F-measure for the asthma class on the holdout set from which it was identified. The gene sets for each of the eight models are shown in Table 4 below, as well as the 275 unique genes in the asthma gene panel are also shown.

Validation of the LR-RFE & Logistic Asthma Gene Panel in an RNAseq Test Set of Independent Subjects

The LR-RFE & Logistic asthma gene panel identified by the machine learning pipeline was then tested on the RNAseq test set (n=40) to assess its performance in independent subjects. F-measure was used to measure performance. For comparison, the same machine learning methodology was used to train and evaluate models from all combinations of feature selection and classification methods considered in the pipeline.

LR-RFE & Logistic Performance Comparison to Alternative Classification Models

To evaluate the relative performance of the LR-RFE & Logistic asthma gene panel, the inventors also applied the machine learning pipeline with replacement of the feature (gene) selection step with these pre-determined gene sets: (1) all filtered RNAseq genes, (2) all differentially expressed genes, and (3) known asthma genes from a recent review of asthma genetics²⁹. These were each used as a predetermined gene set that was run through our machine learning pipeline (FIG. 8 with the feature selection component turned off) to identify the best performing global classification algorithm and the optimal asthma classification threshold for this predetermined set of features. The algorithm and threshold were used to train this gene set's representative classification model over the entire development set, and the optimal model for each of these gene sets was then evaluated on the RNAseq test set in terms of the F-measures for the asthma and no asthma classes. Finally, as a baseline representative of sparse classification algorithms, which represent a one-step option for doing feature selection and classification simultaneously, the inventors also trained an L1-regularized logistic regression model (L1-Logistic)⁶⁹ on the development set and evaluated it on the RNAseq test set.

Performance Comparison to Permutation-Based Random Models

To determine the extent to which the performance of all the above classification models could have been due to chance, the inventors compared their performance with that of random counterpart models (FIGS. 12, 13). These models were obtained by randomly permuting the labels of the samples in the development set and executing each of the feature selection-global classification combinations on these randomized data sets in the same way as described above for the real development set. These random models were then applied to each of the test sets considered in our study, and their performances were also evaluated in terms of the F-measure. For each of real models trained using the combinations, 100 corresponding random models were learned and evaluated as above, and the performance of the real model was compared with the average performance of the corresponding random models.

Validation of the Asthma Gene Panel in External Asthma Cohorts

To assess the generalizability of the asthma gene panel, microarray-profiled data sets of nasal gene expression from two external asthma cohorts—Asthma1 (GSE19187)³⁰ and Asthma2 (GSE46171)³¹ (Table 5)—were obtained from NCBI Gene Expression Omnibus (GEO)⁷⁰. The asthma gene panel was evaluated on these external asthma test sets with performance measured by F-measures for the asthma and no asthma classes.

Validation of the Asthma Gene Panel in External Cohorts with Other Respiratory Conditions

To assess the panel's ability to distinguish asthma from respiratory conditions that can have overlapping symptoms with asthma, microarray-profiled data sets of nasal gene expression were also obtained for five external cohorts with allergic rhinitis (GSE43523)³⁶, upper respiratory infection (GSE46171)³¹, cystic fibrosis (GSE40445)³⁷, and smoking (GSE8987)¹² (Table 6). The asthma gene panel was evaluated on these external test sets of non-asthma respiratory conditions with performance measured by F− measures for the asthma and no asthma classes.

Results

Study Population and Baseline Characteristics

A total of 190 subjects underwent nasal brushing for this study, including 66 subjects with well-defined mild-moderate asthma (based on symptoms, medication use, and demonstrated airway hyperresponsiveness by methacholine challenge response) and 124 subjects without asthma (based on no personal or family history of asthma, normal spirometry, and no bronchodilator response). The definitional criteria we used for mild-moderate asthma were consistent with US National Heart Lung Blood Institute guidelines for the diagnosis of asthma⁷, and are the same criteria used in the longest NIH-sponsored study of mild-moderate asthma^(21,22).

From these 190 subjects, a random selection of 150 subjects were a priori assigned as the development set (to be used for classification model development and biomarker identification), and the remaining 40 subjects were a priori assigned as the RNAseq test set (to be used as one of 8 validation test sets for testing of the classification model and biomarker genes identified with the development set). Assignment of subjects to the development and test sets was done at this early juncture in the study to enable RNA sequencing from all subjects in a single run (to reduce potential bias from sequencing batch effects) with then immediate allocation of the sequence data to the development or test sets prior to any pre-processing and analysis. The test set was then set aside to preserve its independence.

The baseline characteristics of the subjects in the development set (n=150) are shown in the left section of Table 1. The mean age of subjects with and without asthma was comparable, with slightly more male subjects with asthma and more female subjects without asthma. Caucasians were more prevalent in subjects without asthma, which was expected based on the inclusion criteria. Consistent with the reversible airway obstruction that characterizes asthma⁴, subjects with asthma had significantly greater bronchodilator response than control subjects (P=1.4×10−5). Allergic rhinitis was more prevalent in subjects with asthma (P=0.005), consistent with known comorbidity between allergic rhinitis and asthma²³. Rates of smoking between subjects with and without asthma were not significantly different.

RNA isolated from nasal brushings from the subjects was of good quality with mean RIN 7.8 (±1.1). The median number of paired-end reads per sample from RNA sequencing was 36.3 million. Following normalization and filtering, 11,587 genes were used for analysis. VariancePartition analysis²⁴ showed that age, race, and sex minimally contributed to total gene expression variance (FIG. 7).

TABLE 1 Baseline characteristics of subjects in the RNAseq development and test sets Development Set Test Set No No Development All Asthma Asthma All Asthma Asthma vs. test Set P (n = 150) (n = 53) (n = 97) (n = 40) (n = 13) (n = 27) value^(B) Age (years) 26.9 (5.4) 25.7 (2.0) 27.6 (6.5) 26.2 (5.1) 25.3 (2.1) 26.6 (6.1) 0.47 Sex - female 89 (59.3%) 24 (45.3%) 65 (67.0%) 21 (52.5%) 2 (15.3%) 19 (70.4%) 0.40 Race 0.60 Caucasian 116 (77.3%) 21 (40.4%) 96 (99.0%) 32 (80.0%) 5 (38.5%) 27 (100.0%) African 24 (16.0%) 23 (43.4%) 1 (1.0%) 32 (80.0%) 5 (38.5%) 0 (0.0%) American Latino 5 (3.3%) 5 (9.4%) 0 (0.0%) 5 (12.5%) 5 (38.5%) 0 (0.0%) Other 5 (3.3%) 4 (7.5%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) FEV1^(A) (% 94.7% (10.0%) 94.6% (10.9%) 94.8% (9.7%) 94.5% (11.4%) 94.4% (12.0%) 94.6 (11.3%) 0.90 predicted) FEV1/FVC^(A) 82.5% (6.4%) 81.5% (6.7%) 83.1% (6.3%) 82.7% (5.5%) 84.8% (4.4%) 81.6% (5.8%) 0.91 (% predicted) Bronchodilator 5.6% (6.0%) 8.7% (6.4%) 3.9% (5.1%) 4.5% (5.4%) 7.0% (6.1%) 3.3% (4.7%) 0.29 response (%) Age asthma 3.2 (2.7) n/a 3.4 (2.0) 0.78 onset: years Allergic 60 (40.0%) 29 (54.7%) 31 (32.0%) 7 (17.5%) 7 (53.8%) 0 (0%) 0.009 rhinitis Nasal 14 (9.3%) 9 (170%) 5 (5.2%) 0 0 0 0.07 steroids Smoking 7 (4.7%) 1 (1.9%) 6 (6.2%) 1 (2.5%) 0 1 (3.7%) 1.0 ^(A)pre-bronchodilator measures. FEV1 = forced expiratory flow volume in 1 second, FVC = forced vital capacity. Mean (SD) or Number (%) provided. ^(B)Fisher's Exact test for categorical variables and t-test for continuous variables.

Differential gene expression analysis by DeSeq2²⁵, showed that 1613 and 1259 genes were respectively over- and under-expressed in asthma cases versus controls (false discovery rate (FDR)≤0.05) (Table 2A-2B). These genes were enriched for disease-relevant pathways²⁶ including immune system (fold change=3.6, FDR=1.07×10−22), adaptive immune system (fold change=3.91, FDR=1.46×10−15), and innate immune system (fold change=4.1, FDR=4.47×10−9) (Table 2A-2B).

Identification of the Asthma Gene Panel by Machine Learning Analyses of RNAseq Development Set

To identify gene expression biomarkers that accurately predict asthma status, the inventors developed a nested machine learning pipeline that combines feature (gene) selection¹⁸ and classification¹⁹ techniques (FIG. 8). The first component of the pipeline used a nested (inner and outer) cross-validation protocol²⁷ for selecting predictive sets of features (FIG. 8). For this, the inventors used the Recursive Feature Elimination (RFE) algorithm¹⁸ combined with L2-regularized Logistic Regression (LR or Logistic) and Support Vector Machine (SVM (with Linear kernel))¹⁹ classification algorithms (the combinations are referred to as LR-RFE and SVM-RFE respectively). Asthma classification models were then learned by applying four global classification algorithms (SVM-Linear, AdaBoost, Random Forest, and Logistic) to the expression profiles of the selected genes. This learning and evaluation process was run over 100 training-holdout splits of the development set. All resulting models were statistically compared²⁰ in terms of their performance and parsimony (i.e., number of feature/gene sets included in the model) (FIG. 10A-10B). Performance was measured in terms of F-measure²⁸, a conservative mean of precision and sensitivity. F-measure ranges from 0 to 1, with higher values indicating superior classification performance. A value of 0.5 for F-measure does not represent a random model. To estimate random performance, the inventors trained and evaluated permutation-based random models as described herein. Given the central role that F-measure plays in the interpretation of these results, a detailed explanation of F-measure and its relation to more common performance measures is provided below and in FIG. 11.

Evaluation Measures for Predictive Models

The most commonly used evaluation measures for predictive models in medicine are the positive and negative predictive values (PPV and NPV respectively). As shown in FIG. 11, PPV and NPV are equivalent to precisions²⁸ for the positive and negative classes (asthma and no asthma in our study) respectively. However, relying solely on predictive values (i.e., precisions) ignores the critical dimension of the sensitivity or recall²⁸ (also defined in FIG. 11) of the test. For instance, the test may predict perfectly for only one asthma sample in a cohort and make no predictions for all other asthma samples. This will yield a PPV of 1, but poor sensitivity/recall. Thus, for all tasks involving evaluation of asthma classification models in our study, F-measure (FIG. 11) was used as the main performance measure. This measure, which is a harmonic (conservative) mean of precision and recall that is computed separately for each class, provides a more comprehensive and reliable assessment of model performance. Furthermore, unlike area under the receiver operating characteristic (ROC) curve (AUC), F-measure is the preferred metric for classification performance when case and control groups are not balanced (i.e., 1:1)²⁸, which is frequently the case in clinical studies and medical practice. Like AUC, F-measure ranges from 0 to 1, with higher values indicating superior classification performance. However, unlike AUC, a value of 0.5 for F-measure does not represent a random model and could in some cases indicate superior performance over random. F-measures for random performance for specific datasets and models can be estimated using permutation-based random models as described herein.

A combination with good precision and recall determined from this comparison was LR-RFE & Logistic (FIG. 10A, 10B), as the models learned using this feature selection and classification model were able to obtain the best performance with the fewest number of selected genes. This combination used the logistic regression algorithm¹⁹ as both the feature selection algorithm and global classification algorithm. The model learned using this combination, built upon an optimal set of 90 predictive genes, had perfect F-measures (F=1.00) in classifying asthma and no asthma in its corresponding holdout set. This model also significantly outperformed permutation-based random models The other seven classification models listed in Table 4 also had good precision and recall with the asthma gene panel.

Forty six of the 90 genes included in the LR-RFE & Logistic model were differentially expressed genes, with 22 and 24 genes over- and under-expressed in asthma, respectively (FIG. 6 and Table 2A-2B). The remaining 44 genes were not differentially expressed. These results support that the machine learning pipeline was able to extract information beyond differentially expressed genes, allowing for the identification of a parsimonious panel of genes that together allowed for accurate asthma classification. Among these 90 genes, only four (C3, DEFB1, CYFIP2 and GSTT1) are known asthma genes³⁷. This demonstrates that the invented methodology effectively mines data to discover predictive genes that would not have been found by relying exclusively on current domain knowledge.

The LR-RFE & Logistic model of 90 genes is a subset of the 275 unique genes identified in all eight models, which 275 genes are defined as the “asthma gene panel”. Preferably, the 90 genes in this LR-RFE & Logistic asthma gene panel are used in combination with the LR-RFE & Logistic classifier and the model's optimal classification threshold (classify as asthma if probability output ≥about 0.76, else no asthma) to be effectively used for asthma classification, diagnosis or detection. Similarly, the genes in the model-specific asthma gene panels (Table 4) are used in combination with their model-specific classifiers and the model-specific optimal classification threshold to classify, diagnose or detect asthma effectively.

Validation of the Asthma Gene Panel in an RNAseq Test Set of Independent Subjects

The inventors tested the asthma gene panel identified from the above-described machine learning pipeline on an independent RNAseq test set. For this step, the inventors used the test set (n=40) of nasal RNAseq data from independent subjects that was set aside and remained untouched by the development set analysis. The baseline characteristics of the subjects in the test set (n=40) are shown in the right section of Table 1. The baseline characteristics were similar between the development and test sets, except for a lower prevalence of allergic rhinitis among those without asthma in the test set.

The LR-RFE & Logistic Model asthma gene panel performed with high accuracy in the RNAseq test set of independent subjects, achieving AUC=0.994 (FIG. 2). The panel achieved high positive predictive value (PPV) of 1.00 and negative predictive value (NPV) of 0.96. Given imbalances in the case and control groups, F-measure is the preferred and more conservative metric for classification performance (FIG. 1). The asthma gene panel achieved F=0.98 and 0.96 for classifying asthma and no asthma respectively (FIG. 3, left set of bars). For comparison, the much lower performance of permutation-based random models is shown in FIG. 12.

As context for comparison to other models possible from the machine learning pipeline and other methods, FIG. 4 shows the performance of the 90-gene LR-RFE & Logistic model in the test set relative to those of classification models built using (1) other combinations tested in the machine learning pipeline, (2) all genes after filtering (11587 genes), (3) differentially expressed genes (Table 2A-2B), (4) 70 known asthma genes²⁹ (Table 3) and (5) a commonly used one-step classification model (L1-Logistic, 243 genes). All these models performed significantly better than their random counterparts. The LR-RFE & Logistic Model asthma gene panel performed consistently among all the models derived from the machine learning pipeline, as had been expected based on the extensive training and analysis on the development set. The LR-RFE & Logistic Model asthma gene panel also outperformed the model learned using the one-step L1-Logistic method. By separating the feature/gene selection and (outer) classification components, the machine learning pipeline was able to learn a more accurate and more parsimonious classification model, both of which are valuable qualities for disease classification, than L1-Logistic. Overall, these results confirmed that the performance of the LR-RFE & Logistic Model asthma gene panel translated to an independent RNAseq test set, more so than other models, thus lending confidence to this LR-RFE & Logistic Model panel's ability to classify asthma accurately.

Similarly, the other seven classification models and corresponding asthma gene panels performed well in terms of precision and recall, and also beat random performance, such that these models also classify asthma accurately.

Validation of the LR-RFE & Logistic Model Asthma Gene Panel in External Asthma Cohorts

To test the generalizability of the LR-RFE & Logistic Model asthma gene panel for asthma classification, the inventors applied this model to gene expression array data sets generated from two independent cohorts by other investigators with and without asthma (Asthma1GEO GSE19187)³⁰ and Asthma2 (GEO GSE46171)²¹.). Table 5 summarizes the characteristics of these external independent test sets. These datasets were generated from nasal samples collected by independent investigators from subjects with and without asthma from distinct populations, which were then profiled on gene expression microarray platforms. In general, RNA-seq based predictive models are not expected to translate to microarray profiled samples.^(32,33) Gene mappings do not perfectly correspond between RNAseq and microarray due to disparities between array annotations and RNAseq gene models³³. The goal was to assess the performance of the LR-RFE & Logistic Model asthma gene panel despite the discordance of study designs, sample collections, and gene expression profiling platforms.

The inventors found that the LR-RFE & Logistic Model asthma gene panel performed relatively well given the above handicaps, and better than expected in classifying both asthma and no asthma (FIG. 3, middle and right set of bars) and with significantly better performance than permutation-based random models (FIG. 12). In particular, the LR-RFE & Logistic Model asthma gene panel markedly outperformed random models in classifying no asthma in both the Asthma1 and Asthma2 test sets. While classification of asthma in Asthma2 achieved an F-measure of 0.74, its random counterpart also performed well (FIG. 12). Asthma2 included many more asthma cases than controls (23 vs. 5). In such a skewed data set, it is possible for a random model to yield an artificially high F-measure for the majority class (here asthma) by predicting every sample to belong to that class. The inventors verified that this occurred with this random model. These results show that the LR-RFE & Logistic Model asthma gene panel performed reasonably well in these microarray test sets, supporting a degree of generalizability of the panel across platforms and cohorts. Such a translatable result has not been observed very frequently in translational genomic medicine research^(34,35).

The LR-RFE & Logistic Model Asthma Gene Panel is Specific to Asthma: Validation in External Cohorts with Non-Asthma Respiratory Conditions

Because symptoms of asthma often overlap with those of other respiratory diseases, the inventors next sought to test the specificity of the LR-RFE & Logistic Model gene panel to asthma classification. For this, the inventors evaluated the performance of this LR-RFE & Logistic Model panel on nasal gene expression data derived from case control cohorts with allergic rhinitis (GSE43523)³⁶, upper respiratory infection (GSE46171)³¹, cystic fibrosis (GSE40445)³⁷, and smoking (GSE8987)¹². Table 6 details the characteristics for these external cohorts with non-asthma respiratory conditions. In four of the five non-asthma data sets, the LR-RFE & Logistic Model asthma gene panel appropriately produced one-sided classifications, i.e., all samples were classified as “no asthma” or healthy, the term for the control class (FIG. 5). Specifically, the positive predictive value of the LR-RFE & Logistic Model panel across these test sets was exactly and appropriately zero for these test sets of non-asthma respiratory conditions (Table 7). The one exception to this was upper respiratory infection (URI2) profiled on day 2 of the illness, where the LR-RFE & Logistic Model panel classified some samples as asthma (F=0.25). This may have been influenced by common inflammatory pathways underlying early viral inflammation and asthma³⁸. Nonetheless, consistent with the other non-asthma test sets, the panel's misclassification of URI2 as asthma was substantially less than its random counterparts (FIG. 13). These results show that the invented method is specific for classifying asthma and would not misclassify other respiratory diseases as asthma.

Examination of Genes in the LR-RFE & Logistic Model Asthma Gene Panel

Forty-six of the 90 genes included in the LR-RFE & Logistic Model panel were differentially expressed (FDR≤0.05), with 22 and 24 genes over- and under-expressed in asthma respectively (FIG. 6, Table 2A-2B). More generally, the genes in LR-RFE & Logistic Model panel had lower differential expression FDR values than other genes (Kolmogorov-Smirnov statistic=0.289, P-value=2.73×10−37) (FIG. 14). Pathway enrichment analysis of these 90 genes was statistically limited by the small number of genes, yielding enrichment for pathways including defense response (fold change=2.86, FDR=0.006) and response to external stimulus (fold change=2.50, FDR=0.012). Only four (C3, DEFB1, CYFIP2 and GSTT1) of the 90 genes are known asthma genes and are functionally involved in complement activation, microbicidal activity, T-cell differentiation, and oxidative stress, respectively²⁹. These results suggest that the machine learning pipeline was able to extract information beyond individually differentially expressed or previously known asthma genes, allowing for the identification of a parsimonious panel of genes, including the LR-RFE & Logistic Model panel, that collectively enabled accurate asthma classification.

Discussion

The inventors have identified a panel of genes, as well as subsets of these genes for use with specific classifiers, expressed in nasal epithelium that accurately classifies subjects with mild/moderate asthma from healthy controls. This asthma gene panel, consisting of 275 unique genes interpreted via eight logistic regression classification models, performed with good precision and sensitivity. Specifically, the LR-RFE & Logistic model and associated asthma gene panel performed with high precision (PPV=1.00 and NPV=0.96) and sensitivity (0.92 and 1.00 for asthma and no asthma respectively) for classifying asthma. The performance of the LR-RFE & Logistic Model asthma gene panel across independent asthma test sets supports the generalizability of this panel across different study populations and two major modalities of gene expression profiling (RNA sequencing and microarray), as well as the specificity of this LR-RFE & Logistic Model panel as a diagnostic tool for asthma in particular, as well as the gene panels identified by the other seven models as discussed herein.

The asthma gene panel has high potential to be used as a minimally invasive biomarker to aid in asthma diagnosis in children and adults, as it can be quickly obtained by simple nasal brush, does not require machinery for collection, and is easily interpreted. According to the Global Initiative for Asthma and US National Heart Lung Blood Institute, the diagnosis of asthma should be based on a history of typical symptoms and objective findings of variable expiratory airflow limitation by PFT^(6, 7). Practically, however, objective findings are often not obtainable. Patients with mild/moderate asthma are frequently asymptomatic at the time of the clinical encounter, so they may have no detectable wheezing or cough on exam. Pulmonary function testing (PFT) is often not done for patients, as was keenly demonstrated by a study showing that over half of 465,866 patients age 7 years and older with newly diagnosed with asthma had no PFTs performed within a 3.5 year time period surrounding the time of diagnosis.⁸ Clinicians may defer PFTs due to lack of equipment, time, and/or expertise to perform and interpret results^(8, 9). Diagnosing asthma based on history alone contributes to its under-diagnosis, as patients with asthma under-perceive and under-report their symptoms¹¹. Misdiagnosis of asthma also occurs frequently given overlapping symptoms between asthma and other conditions³⁹. Even if PFTs are obtained, spirometric abnormalities in mild/moderate asthmatics are not always present. An objective, accurate diagnostic tool that is easy and quick to obtain and interpret with minimal effort required by the provider and patient could improve asthma diagnosis so that appropriate management can be pursued. The nasal brush-based asthma gene panel meets these biomarker criteria.

Implementation of the asthma gene panel could involve clinicians brushing a patient's nose, placing the brush in a prepackaged tube, and submitting the sample for gene expression profiling targeted to the panel. Some platforms allow for direct transcriptional profiling of tissue without an RNA isolation step, avoiding inconveniences associated with direct RNA work^(40, 41) and yielding comparable results to RNAseq⁴². Bioinformatic interpretation of the output via the LR-RFE & Logistic model and classification threshold could be automated, resulting in a determination of asthma or no asthma for the clinician to consider. Biomarkers based on gene expression profiling are being successfully used in other disease areas (e.g., MammaPrint⁴³ and Oncotype DX⁴⁴ for diagnosing/predicting breast cancer phenotypes).

Because it takes seconds for nasal brushing, the panel may be attractive to time-strapped clinicians, particularly primary care providers at the frontlines of asthma diagnosis. Asthma is frequently diagnosed and treated in the primary care setting⁴⁵ where access to PFTs is often not immediately available. Although PFTs yield results without specimen handling, these advantages do not seem to overcome its logistical limitations as evidenced by their low rate of real-life implementation, 9 but low cost⁴⁶. However, gene expression profiling costs are likely to decrease47, and implementation of the LR-RFE & Logistic Model asthma gene panel could result in cost savings if it reduces the under-diagnosis and misdiagnosis of asthma³. Undiagnosed asthma leads to costly healthcare utilization worldwide³, including in the United States, where asthma accounts for $56 billion in medical costs, lost school and work days, and early deaths⁴⁸. Clinical implementation of the asthma gene panel could identify undiagnosed asthma, leading to its appropriate management before high healthcare costs from unrecognized asthma are incurred. Given the the LR-RFE & Logistic Model panel's demonstrated specificity, use of the LR-RFE & Logistic Model asthma gene panel could also reduce asthma misdiagnosis by correctly providing a determination of “no asthma” in non-asthmatic subjects with conditions often confused with asthma. Clinical benefit from gene-expression based biomarkers has already been seen in the breast cancer field, where use of the 70-gene panel test MammaPrint to guide chemotherapy in a clinical trial leads to a lower 5-year rate of survival without metastasis compared to standard management⁴³.

The nasal brush-based asthma gene panel capitalizes on the common biology of the upper and lower airway, a concept supported by clinical practice and previous findings.¹²⁻¹⁵ Clinically, clinicians rely on the united airway by screening for lower airway infections (without limitation, influenza, methicillin-resistant Staphylococcus aureus) with nasal swabs.⁴⁹ Sridhar et al. found that gene expression consequences of tobacco smoking in bronchial epithelial cells were reflected in nasal epithelium.¹² Wagener et al. compared gene expression in nasal and bronchial epithelium from 17 subjects, finding that 99.9% of 33,000 genes tested exhibited no differential expression between nasal and bronchial epithelium in those with airway disease.¹³ In a study of 30 children, Guajardo et al. identified gene clusters with differential expression in exacerbated asthma vs. controls.¹⁴ The above studies were done with small sample sizes and microarray technology, although more recently, Poole et al. compared RNA-seq profiles of nasal brushings from 10 asthmatic and 10 control subjects to publically available bronchial transcriptional data, finding strong correlation (ρ=0.87) between nasal and bronchial transcripts, and strong correlation (ρ=0.77) between nasal differential expression and previously observed bronchial differential expression in asthmatics.¹⁵

Although based on only 90 genes, the LR-RFE & Logistic Model asthma gene panel classified asthma with greater accuracy than models using all differentially expressed genes in the sample (n=2187), all known asthma genes from genetic studies of asthma (n=70), as well as models based on information from all sequenced genes (n=11587 after filtering) (FIG. 4). Its superior performance supports that the machine learning pipeline described herein successfully selected a parsimonious set of informative genes that (1) captures more actionable knowledge than those identified by traditional differential expression and genetic analyses, and (2) cuts through the noise of genes that are irrelevant to asthma. The genes selected by the other seven models listed in Table 4 are also highly precise and have good recall. About half the genes in the LR-RFE & Logistic Model asthma gene panel were not differentially expressed at FDR≤0.05, and as such would not have been examined with greater interest if the inventors had performed only differential expression analysis, which is the main analytic approach of virtually all studies of gene expression in asthma.^(12-15, 50, 51) The differential expression FDRs of the 90 genes in the LR-RFE & Logistic Model panel were skewed toward lower values as compared to the rest of the genes in our development set (FIG. 14). This demonstrated that the LR-RFE & Logistic Model asthma gene panel captures signal from differential expression as well as genes below traditional significance thresholds that may still have a contributory role in asthma classification. Only four of the 90 genes in the LR-RFE & Logistic Model gene panel (complement component 3 (C3), defensing beta-1 (DEFB1), cytoplasmic FMR1 interacting protein (CYFIP2) and glutathione S-transferase theta 1 (GSTT1) were genes previously identified by genetic association studies.²⁹In this study, the inventors were able to use the machine learning pipeline to identify this LR-RFE & Logistic Model panel of 90 genes—comprised of both differentially expressed and non-differentially expressed genes, and of genes largely without known genetic associations with asthma—whose gene expression levels can be jointly interpreted via a logistic regression algorithm to accurately predict asthma status.

The asthma gene panel did not perform quite as well in the asthma microarray test sets, and this was to be expected due to differences in study design between the RNAseq and and microarray test sets. First, the baseline characteristics and phenotyping of the subjects differed. Subjects in the RNAseq test set were adults who were classified as mild/moderate asthmatic or healthy using the same strict criteria as the development set (see Materials and Methods above), which required subjects with asthma to have an objective measure of obstructive airway disease (i.e., positive methacholine challenge response). In contrast, subjects in the Asthma1 microarray test set were all children (i.e., not adults) with underlying allergic rhinitis and dust mite allergen 358 sensitivity, whose asthma status was then determined clinically³⁰ (Table 5). Subjects from the Asthma2 cohort were adults who were classified as having asthma or as healthy based on history. As mentioned, the diagnosis of asthma based on history alone without objective lung function testing can be inaccurate⁵². The phenotypic differences between these test sets alone could explain the differences in performance of the LR-RFE & Logistic Model asthma gene panel in the microarray test sets. Second, the differential performance may be due to the difference in gene expression profiling approach. Gene mappings do not perfectly correspond between RNAseq and microarray due to disparities between array annotations and RNAseq gene models.³³ Compared to microarrays, RNAseq quantifies more RNA species and captures a wider range of signal.⁵⁰ Prior studies have shown that microarray-derived models can reliably predict phenotypes based on samples' RNAseq profiles, but the converse does not often hold.³³ Despite the above limitations, the asthma gene panel (identified using the RNAseq-derived development set) performed with reasonable accuracy in classifying asthma in the independent microarray test sets. These results support the generalizability of the asthma gene panel to asthma populations that may be phenotyped or profiled differently.

An effective biomarker for clinical use should have good positive and negative predictive value.⁵³ In the present method, if an individual has asthma, the ideal biomarker would confirm this most of the time so that an accurate diagnosis is made, and if an individual does not have asthma, the ideal biomarker would confirm this (indicating “no asthma”) so that misdiagnosis does not occur. This is indeed the case with the LR-RFE & Logistic Model asthma gene panel, which achieved high positive and negative predictive values of 1.00 and 0.96 respectively on the RNAseq test set. The inventors tested the LR-RFE & Logistic Model asthma gene panel on independent tests sets of subjects with upper respiratory infection, cystic fibrosis, allergic rhinitis, and smoking, showing that the panel had a low to zero rate of misclassifying subjects with these other respiratory conditions as having asthma (FIG. 5). These results were particularly notable for allergic rhinitis, a predominantly nasal condition. Although the asthma gene panel is based on nasal gene expression, and asthma and allergic rhinitis frequently co-occur²³, the LR-RFE & Logistic Model panel did not misdiagnose allergic rhinitis as asthma. These results support the specificity of the LR-RFE & Logistic Model asthma gene panel, as well as the gene panels identified in the other models, as a diagnostic tool for asthma in particular.

Even though the development set was from a single center and its baseline characteristics do not characterize all populations, variancePartition analysis demonstrated minimal contribution of age, race, and gender to gene expression variance in these data (FIG. 7). Further, the LR-RFE & Logistic Model panel performed well in multiple external data sets spanning children and adults of varied racial distributions, and with asthma and other respiratory conditions defined by heterogeneous criteria. Subjects with asthma in the development cohort were not all symptomatic at the time of sampling. The fact that the performance of the LR-RFE & Logistic Model asthma gene panel does not rely on symptomatic asthma is a strength, as many mild/moderate asthmatics are only sporadically symptomatic given the fluctuating nature of the disease.

As with any disease, the first step is to accurately identify affected patients. The asthma gene panel described in this study provides an accurate path to this critical diagnostic step. With a correct diagnosis, an array of existing asthma treatment options can be considered⁶. A next phase of research will be to develop a nasal biomarker to predict endotypes and treatment response, so that asthma treatment can be targeted, and even personalized, with greater efficiency and effectiveness⁵⁴.

In summary, the inventors applied a machine learning pipeline to identify a panel of genes expressed in nasal epithelium that accurately classifies subjects with mild/moderate asthma from healthy controls. This asthma gene panel, comprised of 275 genes and/or its subsets used in combination with model-specific classifiers and model-specific optimal classification thresholds, performed with accuracy across 8 independent test sets, demonstrating generalizability across study populations and gene expression profiling modality, as well as specificity to asthma. The asthma gene panel has high potential to be used as a minimally invasive biomarker to aid in asthma diagnosis, as it can be quickly obtained by simple nasal brush, does not require machinery for collection, and is easily interpreted. There are currently many limitations in asthma diagnostics. If applied to clinical practice, this asthma gene panel could improve asthma diagnosis and classification, reduce incorrect diagnoses, and prompt appropriate therapeutic management.

Table 2. Lists of over-expressed (A) and under-expressed (B) genes and pathways in asthma cases as compared to controls. Differentially expressed genes were identified using DESeq2²⁵ and enriched pathways were identified from the Molecular Signature Database²⁶.

TABLE 2A Over-expressed Genes and Pathways Fold Gene/Pathway Change/Description FDR SDK1 2.69593084 5.40181E−20 ZDHHC1 2.33556546 1.23118E−19 SSBP4 2.16530278 2.57344E−19 C10orf95 3.09615627  3.8891E−18 ZNF853 3.05377899 2.25024E−15 PRRT3 1.97782866 2.40254E−15 ODF3B 3.0809781 3.64261E−15 BZRAP1 2.42875066 3.96241E−15 HAGHL 4.04252549 7.90746E−15 CROCC 3.12056593 8.21575E−15 C6orf108 1.8717848 8.86186E−15 PTPRN2 2.24409883 1.20755E−14 SERPINF1 2.03790903 1.47636E−14 P4HTM 2.12086604 1.86794E−14 C19orf51 4.6822365 3.60797E−14 ZSCAN18 2.59451449 3.60797E−14 B9D2 2.07415317 3.60797E−14 ARHGAP39 2.49865011 5.35894E−14 FOXJ1 4.26776351 5.88781E−14 LRRC10B 4.42558987  6.5261E−14 CCDC42B 4.2597176  6.5261E−14 GAS2L2 4.70879795 7.82923E−14 C6orf154 3.9015674 8.44201E−14 GLIS3 2.36625326 1.00754E−13 LRRC61 2.06053632 1.09813E−13 ENDOG 1.97993156 1.71162E−13 IRX3 1.83337486 2.01018E−13 CAPS 4.06302266 2.40086E−13 LPHN1 2.10407317 2.68055E−13 C2orf55 2.27283672 3.17873E−13 SYNGAP1 2.13301423 4.22489E−13 CCDC24 1.96494776 4.42276E−13 SLC16A11 2.0521962 4.51489E−13 UCKL1.AS1 3.82462625 6.69507E−13 RRAD 3.39266415 6.69507E−13 NHLRC4 4.55169722 7.65957E−13 PRR7 2.91887265 7.94092E−13 RAB3B 4.24372545 8.15138E−13 CCDC17 4.24211711 8.23826E−13 ANKRD54 2.03165888 9.41636E−13 TCTEX1D4 4.30165643 9.81969E−13 PPP1R16A 1.78187416 1.01874E−12 NAT14 3.06261532 1.03487E−12 CTXN1 4.61823126 1.03958E−12 ANKK1 2.06364461 1.03958E−12 MAPK15 4.61083061 1.07813E−12 TEKT2 4.78797511 1.13157E−12 CCDC96 2.89251884 1.13157E−12 CXCR7 2.57340048 1.18772E−12 SPEF1 4.04138282 1.28995E−12 C2orf81 3.88312294 1.62387E−12 TPPP3 4.1122218 1.95083E−12 TP73 3.73216045 2.05602E−12 C17orf72 4.12597857 2.42931E−12 KIF19 4.04831578 2.42931E−12 CRNDE 1.90266433 2.42931E−12 FDXR 1.75411331 2.42931E−12 TNFAIP8L1 3.66812001 2.52964E−12 IFT140 2.56011824 2.52964E−12 FBXW9 2.0309423 3.71669E−12 ESPN 1.78254716 4.12128E−12 DFNB31 1.8555535  4.1682E−12 TTLL10 3.97446989 4.96622E−12 FAM116B 2.76115746 5.75046E−12 CCDC19 3.97176187 5.83187E−12 C6orf27 3.15382185 6.10565E−12 C16orf48 2.28318997 6.26965E−12 GAS8 1.96553042 6.26965E−12 CD164L2 3.21331723 6.36707E−12 CCDC78 4.79072783 6.85549E−12 CCDC40 4.02185553 7.85218E−12 CCDC157 2.50320674 1.03363E−11 UBXN11 2.67485867 1.12753E−11 C9orf24 4.24049927 1.13692E−11 B9D1 2.93782564  1.3303E−11 LRRC56 2.57381093 1.60583E−11 PKIG 2.47239105 1.60583E−11 ADSSL1 1.963967 1.70739E−11 PASK 2.00442189 1.93192E−11 C5orf49 3.85710623 1.95595E−11 TUBB2C 2.04908703 2.17307E−11 HSPBP1 1.8050605 2.17307E−11 DLEC1 4.80156726 2.39955E−11 ANKMY1 2.5681388 2.39955E−11 RUVBL2 1.8875842 2.41852E−11 WDR54 3.54079973 2.48129E−11 CCDC108 4.40594345 2.82076E−11 USP2 2.61579764 2.82076E−11 WDR90 2.25341462 3.47445E−11 SLC1A4 1.7743007 3.60414E−11 ISYNA1 1.78188864 3.90247E−11 LRRC48 4.23655785 4.33546E−11 SLC27A2 1.77294486 4.33546E−11 C11orf16 4.16123887 4.35926E−11 BBS5 2.05305886 4.96429E−11 C14orf79 1.9431267 4.96429E−11 DNAAF2 1.82683937 5.32802E−11 IQCD 2.99396253  5.9179E−11 PPOX 2.466844  5.9179E−11 ZNF703 1.80994279 6.27934E−11 IGFBP2 2.12208723  6.3397E−11 KCNH3 3.74731532 6.67127E−11 RHPN1 2.11269443 6.74204E−11 KNDC1 4.27320927 8.33894E−11 TRAF3IP1 1.80219185 8.80362E−11 FAM92B 3.96288061 8.91087E−11 C5orf4 2.02530771 9.38443E−11 MAP6 4.48787026 9.67629E−11 IQCE 1.88795828 9.71132E−11 INPP5E 1.8396103 9.71132E−11 NWD1 3.99394282 1.13238E−10 DNAH9 4.39061797 1.16455E−10 LTBP3 1.62487623  1.3309E−10 CDK20 2.3240984 1.54953E−10 CCNO 2.32391131 1.55262E−10 RAB36 3.80755493 1.59581E−10 WDR34 1.87639055 1.87132E−10 DNAI1 4.84949642 2.12635E−10 DNAAF1 3.83746993 2.14037E−10 CCDC164 4.2557065 2.20169E−10 ASCL2 2.04147055 2.26234E−10 FHAD1 3.13964638 2.37682E−10 FAM179A 4.66078913 2.37965E−10 TEKT1 4.13606595 2.48284E−10 DALRD3 1.75343551 2.48284E−10 TMCC2 1.90615943 2.60427E−10 CCDC114 4.09401076 2.95477E−10 LRWD1 1.98021375 3.02767E−10 NCRNA00094 2.12505456 3.12538E−10 WDR38 4.23621789 3.26822E−10 ALDH3B1 1.6813904 3.28037E−10 TMEM190 4.8685534 3.30569E−10 ULK4 2.32420099 3.48495E−10 DMRT2 1.82662574 3.48718E−10 C9orf171 3.97704489 3.72441E−10 FUZ 2.72661607 3.81064E−10 VWA3A 4.21877596 4.49516E−10 CDHR4 5.12021012 4.57757E−10 METRN 2.25309804 4.57757E−10 LOC113230 1.81478964 4.57757E−10 DNAI2 4.03796529 4.76126E−10 TCTN2 2.40490432 4.95937E−10 FAM166B 3.90791018 5.63709E−10 ZMYND10 3.69143549 6.00928E−10 MZF1 1.76527865 6.58326E−10 ROPN1L 3.43290481 6.64612E−10 APBB1 2.62366455 6.64612E−10 PLEKHB1 3.4214872 6.72995E−10 LRRC23 3.23420407 7.30088E−10 SLC4A8 3.06635647 8.20469E−10 WNT9A 1.97501893 8.98004E−10 CCDC103 3.21531173 9.17894E−10 C20orf85 3.7643551 9.37355E−10 TSNAXIP1 3.67477124 9.47472E−10 DNAH2 3.69841798 9.84984E−10 ZNF474 3.52004876 1.11372E−09 TPPP 2.28275479 1.11372E−09 TMEM231 3.16472296 1.12292E−09 TTC12 1.91008892 1.13249E−09 LDLRAD1 3.56956748 1.15526E−09 CHCHD10 1.87337748 1.18307E−09 RFX2 2.66731378 1.23139E−09 UBXN10 3.25532613 1.26161E−09 IFT172 2.64104339  1.3631E−09 BAIAP3 3.63613461  1.411E−09 EFCAB2 2.69292361 1.42619E−09 C11orf88 3.52355279  1.4444E−09 SLC13A3 2.20805923  1.4444E−09 IFT122 2.04426301 1.48429E−09 NPHP4 1.89172058 1.51209E−09 TXNDC5 1.86619199  1.515E−09 C17orf97 2.35986311 1.62066E−09 WDR16 4.36651228 1.62402E−09 DNALI1 3.46070328 1.63511E−09 NUDT3 1.73970966 1.64286E−09 SMYD2 2.10344741 1.70609E−09 TTC25 3.71446639 2.05596E−09 RBM38 1.61948356  2.1203E−09 GGT7 1.66897144 2.14547E−09 CES1 3.00060938 2.23456E−09 C21orf59 1.72965503 2.26356E−09 CCDC65 3.41519122 2.38892E−09 WDR60 1.90360794 2.48798E−09 UNC119B 1.68295738  2.7675E−09 EML1 3.14662458 2.86572E−09 ODF2 1.77285642 2.88517E−09 C20orf96 3.28661501 2.92408E−09 C21orf2 1.59981088 2.95269E−09 LRRC45 1.73562887  2.9555E−09 LOC100506668 2.17031169 3.52531E−09 GLB1L 2.06829337 3.65952E−09 CCDC74A 3.2798251 3.94098E−09 ABCA2 1.64595295 3.94098E−09 MAP1A 3.30677387 4.49644E−09 C9orf9 3.3529991 4.60478E−09 CHST9 1.75966672  4.8617E−09 MAPRE3 2.07180681 5.32347E−09 RND2 2.18107852 5.44526E−09 DGCR6 1.8288164 5.45688E−09 SNED1 1.88272394 5.83476E−09 LRRC46 4.00288588 5.87568E−09 C16orf71 3.78067833 5.87568E−09 FBXO36 1.97697195 5.87808E−09 STK33 3.32049025 5.97395E−09 FANK1 3.09673143 6.34411E−09 IRF2BPL 1.5943287 6.45821E−09 MEX3D 1.59132125 6.57088E−09 TTC29 3.77710968 7.14688E−09 SPAG17 4.10266721 7.18248E−09 DNAH10 4.05401954 7.37766E−09 C19orf55 1.81580403  7.5128E−09 GNA14 2.3089692 7.76554E−09 GPR162 3.42624459 7.78437E−09 KIF24 2.6517961 8.23367E−09 C6orf97 3.05579163 8.66959E−09 ATP2C2 1.60268251 8.79826E−09 EFHC1 3.13154257 1.00071E−08 C9orf1l6 2.98680162 1.02805E−08 TUBA4B 3.44329925 1.10115E−08 TUB 3.28725084 1.10581E−08 IGFBP5 3.42171001 1.12425E−08 GOLGA2B 1.87746797 1.15371E−08 RAGE 2.48773652 1.16413E−08 UCP2 1.52039355 1.17729E−08 KIAA1407 2.63617454 1.18646E−08 TTC21A 2.5095734 1.20361E−08 C1orf173 3.85335748 1.24014E−08 PSENEN 1.74442606 1.26734E−08 MAPK8IP1 2.43031719 1.31409E−08 WDR52 2.7867767  1.3227E−08 RCAN3 1.67977331 1.32982E−08 REC8 2.71104704 1.35783E−08 KCTD1 1.63948363 1.35783E−08 ZNF579 1.56261805 1.43116E−08 NCALD 2.31903784 1.48365E−08 IFT43 1.8372634  1.6037E−08 GALNS 1.69455658 1.60813E−08 RABL5 2.20299003  1.6314E−08 SLC22A4 2.22553299 1.66879E−08 CC2D2A 3.16499889 1.70886E−08 C12orf75 2.65337293 1.74645E−08 MS4A8B 4.57793875 1.78335E−08 DNAH5 3.74507278 1.82168E−08 LRTOMT 2.78785677 1.91101E−08 C18orf1 1.87715316 1.91101E−08 TRADD 1.56913276 1.97067E−08 C1orf194 3.88158651 1.98158E−08 STOX1 2.81737017 2.04397E−08 SPAG6 3.38226503 2.05137E−08 EFCAB6 3.13972956  2.0547E−08 CDHR3 4.50496815 2.09665E−08 C1orf192 3.27606806 2.13713E−08 ST6GALNAC2 1.69322433 2.13713E−08 CEP250 1.63128892 2.13713E−08 RSPH9 3.5289842  2.2596E−08 RFX3 2.64245161 2.28181E−08 DMRTA2 1.55534501 2.28181E−08 CCDC113 3.00709138 2.33952E−08 TCTN1 2.57027348 2.43901E−08 ZNHIT2 1.68919209 2.59867E−08 NELL2 4.27702275 2.62282E−08 DNAH3 3.76161641 2.68229E−08 RSPH1 3.9078246 2.79364E−08 IPO4 1.62195554 2.83731E−08 OSBPL6 2.51046395 2.86967E−08 NPHP1 3.03497793 2.87686E−08 NPEPL1 1.80587307 2.93319E−08 PCDP1 3.86414265 3.03499E−08 HES6 2.83951527 3.03499E−08 OSCP1 2.46419674 3.16173E−08 C6orf225 2.88981515 3.16232E−08 RDH14 1.85367299 3.20457E−08 WDR31 1.86799234  3.3187E−08 NRSN2 1.72859689 3.33598E−08 CYB5D1 2.01628245 3.53966E−08 FAAH 1.64399385 3.56421E−08 LRRC27 1.81134305 3.62992E−08 CIB1 1.51834252 3.65446E−08 SPPL2B 1.52835317 3.68019E−08 CROCCP2 1.60146337 3.69799E−08 NFIX 1.57340231 3.71894E−08 RIBC1 3.0954211 3.73058E−08 ARMC2 2.45822891 3.73058E−08 KIF9 2.3180051 3.79512E−08 COQ4 1.56458854 3.96258E−08 WDR66 3.18527022 4.13597E−08 KLHL6 3.05051676 4.13597E−08 ANKRD9 1.68315489 4.18769E−08 PPIL6 3.49881233  4.5818E−08 CELSR1 1.5798801 4.61481E−08 ECT2L 3.92659277 4.67195E−08 TMEM107 2.25606657 4.72838E−08 IL5RA 3.38598476 4.91414E−08 SPATA18 3.04142002  5.0583E−08 ZNF865 1.55350931 5.11875E−08 MKS1 1.72625587 5.31129E−08 DNAH12 4.07123221 5.46701E−08 SNTN 3.41828613 5.48011E−08 SNAPC4 1.55079316 5.48488E−08 KLHDC9 2.21375808 5.68972E−08 MTSS1 1.59589799 5.76209E−08 PTRH1 1.64149801 5.78872E−08 C16orf55 2.03868071  5.8729E−08 C7orf57 3.24294862 6.00827E−08 NUDC 1.54151756 6.10697E−08 TNFRSF19 2.20738343 6.27622E−08 IQCG 2.95680296  6.2973E−08 VWA3B 3.70172326 6.30683E−08 KAL1 2.86964004 6.30683E−08 WRAP53 1.93108611 6.30683E−08 CLUAP1 1.88649708 6.34659E−08 PACRG 3.25262251 6.37979E−08 CCDC81 3.4942349 6.42368E−08 AKR7A2 1.57742473 6.47208E−08 KCNE1 3.35236141 6.58782E−08 INHBB 3.2633604 6.79537E−08 PRDX5 1.55465969 6.79537E−08 MYB 1.84122844 6.81621E−08 NEK11 2.74190303 6.81892E−08 RUVBL1 2.00081999 6.99548E−08 SYNE1 2.93233229  7.1936E−08 C17orf79 1.59608063 7.31685E−08 JAG2 2.00848549 7.85574E−08 ACOT2 1.61704514 8.52356E−08 PRSS12 1.60068977 8.62009E−08 PHGDH 2.07652258 8.78686E−08 AK8 2.99751993 8.85495E−08 C11orf49 1.65594025 8.87426E−08 SYT5 3.23619723 9.00219E−08 C3orf15 3.55197982 9.33003E−08 PAX3 1.68131102 9.48619E−08 SHANK2 3.08586078 9.57305E−08 AK7 3.11167056 1.04568E−07 DIXDC1 2.20355836 1.04568E−07 ACCN2 1.63822574 1.04568E−07 TBX1 1.62839701 1.05101E−07 HYDIN 3.64358909  1.0567E−07 C13orf30 3.57465645 1.06437E−07 ANKRD37 2.08781744 1.06496E−07 POMT2 1.77671355 1.06496E−07 C21orf58 3.15402189 1.14416E−07 CNTRL 1.98315627 1.15119E−07 SIX2 1.56975674 1.16144E−07 GLB1L2 1.87516329 1.18115E−07 ZNF440 1.62497497 1.18115E−07 SYTL3 1.60669405 1.18115E−07 ERCC1 1.55757069 1.18115E−07 DNAH1 2.22541262 1.18941E−07 FAM154B 3.2374058 1.20444E−07 EFCAB1 3.41783606 1.24931E−07 BBS1 1.62663444 1.26292E−07 PRUNE2 3.09870519 1.26484E−07 H1FX 1.54347559 1.26484E−07 IFT57 2.02384988 1.27781E−07 ARMC3 3.6866857 1.28185E−07 C1orf201 1.97130635 1.32673E−07 C20orf12 2.16851256 1.35408E−07 FAM183A 3.43889722 1.35507E−07 ZBBX 3.75926958 1.37771E−07 C1orf88 3.33179192 1.44064E−07 EFHB 3.24198197 1.45387E−07 YSK4 3.13700382 1.50138E−07 CCDC60 2.03255306 1.50341E−07 TUSC3 1.69381639 1.50981E−07 CES4A 2.40159419 1.51353E−07 CAP2 2.30419698  1.5299E−07 STOML3 3.56916735 1.54086E−07 PCYT2 1.54216983 1.61706E−07 SLFN13 2.24221791  1.6531E−07 DNAL4 1.73946873  1.6531E−07 C2CD2L 1.53455465 1.65577E−07 IFT46 1.9344197  1.7083E−07 DNAH6 3.67492559 1.74274E−07 RSPH4A 3.32798921 1.74274E−07 DTHD1 3.32521784 1.74542E−07 SLC12A7 1.58126148  1.7563E−07 DPCD 1.93856115 1.76542E−07 DNAH7 3.36255762 1.78119E−07 NTN1 1.52761436 1.78206E−07 CLDN3 1.84043179  1.8233E−07 RHOBTB1 1.75019548 1.87553E−07 APOBEC4 3.28732642  1.8767E−07 FAM174A 1.51418232 1.90288E−07 ARMC9 1.90867648 1.91275E−07 PLTP 1.60313361 1.98108E−07 CCDC146 2.6710312  2.0177E−07 C14orf45 2.54462539 2.13129E−07 OBSCN 1.86629325  2.1622E−07 WDR96 4.51826736  2.1911E−07 SFXN3 1.59966258 2.19516E−07 GALM 1.59756388 2.19516E−07 FAM81B 3.17612876 2.22082E−07 EFEMP2 1.61941953 2.24048E−07 RABL2A 2.30603938 2.28887E−07 WDR78 3.09268044 2.33992E−07 C10orf107 3.16756032 2.44725E−07 C9orf135 2.86769508 2.44725E−07 NEURL1B 2.13311341 2.44782E−07 BCAM 2.0015908 2.44782E−07 PKD1 1.53249813 2.46006E−07 FBRSL1 1.50952964 2.46006E−07 DNAJA4 1.55609308  2.5244E−07 C11orf63 2.22050183 2.53161E−07 MAGIX 1.61223309 2.64993E−07 CLMN 2.07549994 2.87911E−07 TNS1 1.77612203 3.08503E−07 SPA17 2.66711922 3.17135E−07 CRY2 1.54310386 3.48954E−07 IQCA1 2.54545108 3.85583E−07 IFT27 2.00349955 3.85583E−07 C6orf165 3.3160697 3.90768E−07 SPATA6 1.86634548 3.91415E−07 ARMC4 3.33542089 4.12418E−07 MNS1 2.96005772 4.20421E−07 AP2B1 1.82011977 4.27029E−07 ABHD12B 1.65078768 4.58254E−07 RABL2B 2.18769571 4.60153E−07 DNAH11 3.39839639 4.78493E−07 TCTEX1D2 2.32862285 4.92481E−07 SNCAIP 2.15177999 5.25094E−07 PRR15 1.52053242 5.39026E−07 TRAPPC9 1.49825676 5.47471E−07 C11orf70 3.19682649 5.52587E−07 MTSS1L 1.51447468 5.77745E−07 IQCC 1.76671873 5.85222E−07 MIPEP 1.60770446 5.87639E−07 CAPSL 3.22810829 6.13092E−07 FBXO31 1.52038127 6.15582E−07 IGFBP7 3.46134083 6.47155E−07 GLTSCR2 1.39112797 6.63441E−07 CASC1 2.94972846 7.41883E−07 AKAP6 2.21859968 7.65044E−07 CDC14A 1.71863036 7.65644E−07 GPR172B 1.68332351 7.75027E−07 KIF3B 1.53993685 8.08875E−07 NSUN7 1.55243313 8.71403E−07 CBY1 1.69853505 9.10803E−07 MORN2 2.28391481  9.392E−07 FAM134B 2.02733713 9.45965E−07 LRRIQ1 3.26113554 9.58549E−07 ZNF446 1.52395776 9.58549E−07 TTC26 2.53343738 9.80114E−07 CALML4 1.62740933 9.95113E−07 LRP11 1.49024896 1.02382E−06 TMPRSS3 1.80633832 1.04835E−06 MDM1 1.71360038 1.07116E−06 PAQR4 1.56647668 1.16048E−06 SEMA5A 1.65992081 1.18574E−06 IDH2 1.48906176 1.22485E−06 SLC2A4RG 1.473539 1.28937E−06 WDR27 1.86298354 1.29757E−06 MB 1.56393059 1.35535E−06 PLCH1 2.31329264 1.36675E−06 FOXN4 2.43309713 1.49276E−06 CETN2 2.31001093 1.51913E−06 ECI1 1.46030427 1.63719E−06 ACOT1 1.71878182 1.65012E−06 SPEF2 3.00394567 1.69058E−06 ENKUR 3.17038628 1.69235E−06 ANKRD42 1.7433919 1.70496E−06 CSMD1 2.01483263 1.71638E−06 LRRC49 2.42707576 1.81419E−06 LRRC6 2.41771576  2.0278E−06 PDF 1.72789067  2.0278E−06 AP3M2 1.6599425  2.0278E−06 ATP6V0E2 1.51739952 2.23414E−06 CYBASC3 1.47190218 2.47918E−06 MGC2752 1.51302987 2.49691E−06 CTGF 2.44083959 2.53147E−06 NME7 2.30993461 2.56434E−06 ICA1L 1.87405521 2.59186E−06 KIAA1377 2.35492722 2.63213E−06 WNT4 1.62388727 2.66608E−06 CCDC66 1.78966672 2.69319E−06 DMD 1.60710731 2.70822E−06 RGMA 1.77597556 2.76587E−06 BCL7A 1.54768303 2.79246E−06 ARL3 1.52985757 2.88426E−06 FKRP 1.59965333 3.01403E−06 RORC 1.52931081 3.01403E−06 ULK2 1.59698142 3.04102E−06 ACSS1 1.55253699 3.07996E−06 HHAT 1.60739942 3.08587E−06 EFNB3 2.4297676 3.45813E−06 B3GNT9 1.55740701 3.51732E−06 SLC25A4 1.49801843 3.55964E−06 CCDC138 1.80406427 3.56785E−06 PABPN1 1.44608578 3.69532E−06 SMPD2 1.47546999 3.70938E−06 ZNF580 1.47324953 3.73581E−06 OLFML2A 1.68087252  3.7554E−06 C7orf50 1.44237361 3.94008E−06 LEPREL2 1.95758996 3.94011E−06 DZIP3 2.22081454 4.02528E−06 NCRNA00287 1.69130571 4.03026E−06 C3orf67 1.72190896 4.09892E−06 IL17RE 1.48542123 4.16438E−06 DUSP18 1.76643191   4.2E−06 HEATR2 1.53592007   4.2E−06 CERS4 1.46651735 4.55413E−06 EFHC2 2.54152611 4.67467E−06 EBF4 1.50785283 4.71457E−06 SCAMP4 1.44146628 4.91032E−06 HEY1 1.51597477 5.00328E−06 CSPP1 2.05160927 5.01668E−06 NCS1 1.53990962 5.02214E−06 ZNF837 1.67092737 5.22131E−06 CCDC104 1.59507824 5.28987E−06 DNAL1 1.92925734 5.86073E−06 TTC38 1.47562236 5.88772E−06 KIF27 2.05357283 6.13829E−06 THRA 1.49828801 6.16885E−06 GNAL 1.51789304 6.24393E−06 LCA5 2.05878538 6.76347E−06 IDAS 1.71281695 7.04626E−06 KIAA0556 1.48330058 7.50539E−06 PYCR2 1.49939954 7.88147E−06 TRPV4 1.47758825 7.88147E−06 TMEM98 1.46244012 8.21506E−06 DYRK1B 1.445023 8.35968E−06 MEGF8 1.4698702 8.57212E−06 FAM149 1.61900561 8.90473E−06 FTO 1.54233263 9.20995E−06 RBKS 1.66266555 9.25498E−06 ORAI3 1.46516304 9.45553E−06 NDUFAF3 1.44305183 9.66172E−06 C16orf80 1.53411506 1.07805E−05 CCDC34 1.95285314 1.08031E−05 FAM104B 1.64584961 1.08935E−05 NME5 2.35890292  1.0967E−05 SRGAP3 1.51025268 1.10599E−05 ALMS1 1.75968611 1.10615E−05 COL9A2 1.46064849 1.10777E−05 CNTNAP3 1.64650311 1.11243E−05 HDAC10 1.43909133 1.12656E−05 WDR35 1.79775411 1.18311E−05 PRR12 1.44830825 1.24302E−05 SNX29 1.49309166 1.25697E−05 CRIP1 2.21165686 1.25722E−05 SOBP 1.70952245 1.29589E−05 SLC9A3R2 1.38857255 1.31279E−05 PHC1 1.60359663 1.38781E−05 PKN1 1.44709171 1.38781E−05 TRIP13 2.13571915 1.40793E−05 SPAG16 1.5476954 1.41052E−05 TBC1D8 1.64734934 1.44514E−05 METTL7A 1.54943803 1.45491E−05 NPM2 1.64770549 1.49453E−05 TSGA14 1.83369437 1.53621E−05 ABCA3 1.56393698 1.53948E−05 EPB41L4B 1.46546865 1.55092E−05 SCGB2A1 1.85264034 1.58836E−05 WDR69 3.13080652 1.59712E−05 MCAT 1.44452413 1.59712E−05 HSPG2 1.44631976 1.69312E−05 LRRC26 1.74351209 1.73709E−05 KIAA0195 1.42018377 1.73709E−05 RFX1 1.41884581 1.80687E−05 WDR19 1.89888711 1.82737E−05 ANKRD35 1.4184045 1.89416E−05 BBS9 1.59591845 1.90715E−05 CCDC41 1.73056217 1.92145E−05 FARP1 1.43058432 1.92684E−05 NGRN 1.41426222 1.93043E−05 DCAKD 1.5245559 2.01031E−05 KATNAL2 1.83549945 2.03357E−05 AUTS2 1.44446141 2.10708E−05 SLC7A2 2.78449202 2.13078E−05 ZDHHC24 1.41648471 2.14062E−05 SLC41A1 1.52318986 2.14929E−05 C8orf47 1.59908668 2.15109E−05 SHROOM3 1.49391839 2.15542E−05 SUV420H2 1.47743036 2.17189E−05 TMEM132A 1.3601549 2.17189E−05 CITED4 1.54649834 2.21855E−05 LMCD1 1.54313711 2.26856E−05 MAGED2 1.42577997 2.28093E−05 RPGRIP1L 2.30088761 2.32284E−05 MT1X 1.75550879 2.34342E−05 REPIN1 1.40482269 2.35893E−05 DNER 2.54706 2.35943E−05 KATNB1 1.41230234 2.40285E−05 C14orf50 2.0041349 2.42509E−05 IFT88 1.81175502 2.53479E−05 POLQ 1.82761614 2.58084E−05 HSD17B13 2.1583746 2.61563E−05 TSPAN8 1.57248017 2.69759E−05 MAP9 2.17752296 2.70383E−05 CD6 1.66024598 2.70383E−05 CUEDC1 1.44127151 2.70383E−05 PALMD 1.84259482 2.73396E−05 CCDC88C 1.44651505  2.9513E−05 GSTA2 3.04364309 2.99797E−05 LOC728392 2.45352889 3.13987E−05 SOX2 1.42277901 3.25439E−05 WDR73 1.45128947  3.2565E−05 KRT15 1.66470618 3.25997E−05 ARVCF 1.4675952 3.46454E−05 UNC93B1 1.3350195  3.6432E−05 FBF1 1.58227897 3.82227E−05 NLRC3 1.6969175 3.93238E−05 MLF1 2.10274167 3.97233E−05 ACACB 1.49814786 4.01764E−05 ADCY9 1.51669291 4.03583E−05 DIAPH2 1.56970385 4.08846E−05 TCEAL3 1.44291146 4.16479E−05 AGBL5 1.44132278 4.20047E−05 ANKZF1 1.44697405 4.20298E−05 TCEA2 1.52429185 4.23984E−05 BAHCC1 1.49917059 4.27983E−05 SYT17 1.56742434 4.28886E−05 HSD17B8 1.44037694 4.30152E−05 RPS6KA2 1.44445649 4.35723E−05 PHTF1 1.48986592 4.40703E−05 TTC30B 1.71522649 4.43779E−05 TMEM67 2.20416717 4.46512E−05 PYCR1 1.68525202  4.5225E−05 C11orf2 1.34624129  4.7456E−05 PDE8B 2.32876958 4.79301E−05 GAL3ST2 1.52140934 4.82899E−05 MYCL1 1.49285532 4.91023E−05 TULP3 1.50475936 4.92334E−05 FBLN5 1.48050793 4.97709E−05 AMN 1.65761529 4.99842E−05 EVL 1.38952418 5.22713E−05 KLC4 1.40405768 5.24118E−05 WNK2 1.41616046 5.30142E−05 C3orf39 1.45324602 5.54577E−05 LRP4 1.93508583 5.79675E−05 FAM179B 1.49020563 5.79675E−05 DYNC2H1 2.39772393 5.80606E−05 IFT81 1.85697674 6.05797E−05 SYNPO 1.43007758 6.05797E−05 C7orf63 2.2475395 6.07346E−05 LIG1 1.46051313  6.2636E−05 NR2F6 1.37135336 6.26657E−05 PPDPF 1.33519823 6.37715E−05 COQ10A 1.57553325 6.42865E−05 ADPRHL1 1.57602912 6.48279E−05 PLXNB1 1.36748122 6.51603E−05 LIPT2 1.57209714 6.54735E−05 GFER 1.38601943 6.57227E−05 PRAF2 1.48691496 6.62534E−05 MAK 2.11010178  6.6389E−05 LPAR3 1.61372461  6.6389E−05 CEP68 1.43585034 6.86926E−05 MGAT3 1.63032562 6.88196E−05 SELM 1.68910302 6.90845E−05 PRKCDBP 1.75929603 6.95654E−05 GMPR 1.74175023 7.09348E−05 NUDT4 1.66108324  7.1223E−05 TMC4 1.37606676 7.32423E−05 C18orf32 1.4680673 7.49847E−05 BBS4 1.48414852 7.55039E−05 TTC15 1.37927452 7.55039E−05 PCM1 1.44508492 7.57285E−05 AHDC1 1.39404544 7.57907E−05 GPT2 1.37898662 7.83202E−05 KIAA0895 1.83866761 8.00835E−05 UFC1 1.42750311   8.07E−05 EPHX2 1.47972778 8.11114E−05 AGR3 2.49250589 8.14424E−05 STUB1 1.40578727 9.07013E−05 MFSD2A 1.41538916 9.08106E−05 TM7SF2 1.36011903 9.49179E−05 BCAS3 1.39837526 9.50537E−05 GYLTL1B 1.50326839 9.52925E−05 CDT1 1.68706876 9.60694E−05 EDARADD 1.40821946 9.72324E−05 KIAA1841 1.63727867 9.74561E−05 PDLIM4 1.33499063 9.91746E−05 FBXL2 1.70441332 0.000100287 CCP110 1.62862095 0.000100436 PLA2G6 1.41041592 0.000101028 COL4A6 1.81881069 0.000101469 COG7 1.41067778 0.000101469 LSS 1.46102295 0.00010236 PITPNM1 1.36286761 0.00010236 IFT74 1.49355699 0.000102847 SIPA1L3 1.43775294 0.000102847 WDR13 1.31401675 0.000107509 ARMCX2 1.63758171 0.000108288 CKB 1.57645121 0.000109216 STK36 1.48863192 0.000112154 FN3K 1.51834554 0.00011281 LOC81691 1.62456618 0.000114135 FAM108A1 1.31380714 0.000114728 SQLE 1.69434086 0.000119836 KCNQ1 1.33310218 0.000122927 BRF1 1.37864866 0.000124633 PROS1 2.25991725 0.000125307 IGSF10 2.12624227 0.000125978 ZNF358 1.35163158 0.000126256 CHCHD6 1.46348972 0.000133584 CES3 1.45903662 0.000138413 VWA2 1.45385588 0.000138791 TTC5 1.52203224 0.00014006 SLC27A1 1.39126087 0.000141835 CYB561 1.37921792 0.000141835 RPGR 1.85326766 0.000142075 VMAC 1.41981554 0.000146443 IK 1.37718344 0.000148072 CEP89 1.5127697 0.000148549 CEBPA 1.33935794 0.000149104 GPX8 1.72869825 0.00015137 TUT1 1.35214327 0.000152136 PEX6 1.52324996 0.000155204 MT1E 1.67168253 0.000155534 LOC441869 1.43946774 0.000157594 S1PR5 1.51757959 0.0001604 CD81 1.32468108 0.000161488 ENPP5 1.75733353 0.000162553 ZNF204P 1.75883566 0.000165462 C10orf81 1.40543082 0.000165462 C11orf74 1.86106419 0.000171801 CRTC1 1.42765953 0.000172249 DDR1 1.36166857 0.000172682 THSD4 1.53230415 0.000178414 TAF6L 1.35674158 0.000179973 AKD1 1.62744603 0.000180844 LZTFL1 1.71503476 0.000184545 PARP10 1.36830665 0.000189223 ZNF3 1.36744076 0.000189238 SEMA4C 1.40268633 0.000189752 ZNF584 1.48555318 0.000191741 NFATC1 1.38421478 0.000191741 ZNF414 1.39531526 0.000194572 KIAA1797 1.48460385 0.000201377 C22orf23 1.47274344 0.000207275 FAM113A 1.37538478 0.000207701 GAS6 1.41786846 0.000211066 C14orf135 1.50529153 0.000227989 BAIAP2 1.32638974 0.000236186 TUSC1 1.39360539 0.000247174 RSPH3 1.43059912 0.00024733 C14orf142 1.62415045 0.000249361 C13orf15 1.35861972 0.000254195 PAQR7 1.38092355 0.000258484 MCF2L 1.40608658 0.000258709 ZFPM1 1.60585901 0.000259986 PARVA 1.39640833 0.00026033 SMPD3 1.41764514 0.000263709 C7orf41 1.39659057 0.00026517 TSGA10 1.87725514 0.000266725 ATPIF1 1.34495974 0.000269242 TRIM3 1.42603668 0.000269692 CEP290 1.50717501 0.000273516 SCAMP5 1.39934588 0.00027358 8-Mar 1.39016591 0.000274885 TSTD1 1.34032792 0.000279518 ATP6V1C2 1.38396906 0.000296582 BTBD3 1.42834347 0.000299561 DOCK1 1.3556739 0.000307703 TPRXL 1.46505444 0.000308225 C6orf48 1.36829759 0.000312557 RRAS 1.43157375 0.000312601 CTU1 1.70766673 0.000313118 CDON 1.5312556 0.000314033 LRFN3 1.40276367 0.000320189 HHLA2 1.77249829 0.000325631 ATP6V0A4 1.40856456 0.000331973 MAZ 1.33830748 0.000331973 FAM131A 1.37617082 0.000334759 ADCK4 1.35866946 0.000345476 NBPF1 1.42147504 0.000346828 PLCH2 1.34487014 0.000351121 TELO2 1.35293949 0.000352106 ZNF469 1.44727917 0.000378978 LMLN 1.55351859 0.000387955 NINL 1.42267221 0.000388085 PAIP2B 1.46931111 0.000391976 LRP3 1.34600766 0.000397182 ZBTB45 1.38679613 0.000405 AP4M1 1.42014443 0.00041951 CYP2F1 1.38163537 0.000421654 ARHGAP44 1.46862173 0.00042522 ASMTL 1.29539878 0.000447663 THNSL2 1.45304585 0.000449374 PWWP2B 1.28979929 0.000449374 ALDH1L1 1.33944749 0.000453928 LRFN4 1.35765376 0.000458695 ANKRD16 1.50341162 0.000468893 ABCB11 1.85720038 0.000469016 PSPH 1.54491063 0.000469099 STRA6 1.61958548 0.00046936 GRTP1 1.3780124 0.00046936 COL6A1 1.90548754 0.00047228 LOC100506990 2.06901283 0.000472754 KIAA1009 1.47960091 0.00047416 SYTL1 1.29291891 0.000484701 HES4 1.54693182 0.000487686 NEIL1 1.45846006 0.000487686 AZI1 1.40092743 0.000487686 KIAA1737 1.39523823 0.000491958 TTLL5 1.41074741 0.000504884 SEPW1 1.29723354 0.000509229 MXD4 1.32904467 0.000509323 PCSK6 1.8750067 0.000512777 NQO1 1.40130035 0.000519124 DAK 1.38150961 0.000524279 SPATA7 1.57805661 0.000530373 ADARB2 1.68685402 0.000530837 PODXL2 1.36921797 0.000554801 UGT2A2 1.66808039 0.000555928 NDN 1.45098648 0.000557146 UBAC1 1.32525498 0.000558971 ERI3 1.36918331 0.000561446 MESDC1 1.32459189 0.000561446 FAM13A 1.45037916 0.000562906 CABIN1 1.37646627 0.000581908 KIAA0649 1.35151381 0.000585764 SBK1 1.42410101 0.000586514 NUDT14 1.40941995 0.000597249 C12orf52 1.36403577 0.000605472 FAM107A 1.81948041 0.000607395 NME2 1.35909489 0.000612032 RAVER1 1.33417287 0.000638651 BOC 1.41111691 0.000639409 MICAL3 1.44407861 0.000645699 HN1L 1.36453955 0.000651034 PTPRT 1.66764096 0.000651183 ZBTB4 1.3320744 0.000652514 MIB2 1.34379905 0.000656935 DST 1.42878897 0.000667193 LRIG1 1.37999443 0.000669593 ENOSF1 1.41462382 0.000670299 IGSF8 1.33768199 0.000680086 MXRA7 1.30938141 0.00069497 THOP1 1.37339684 0.000712132 ZNF688 1.51336829 0.000716478 GDPD5 1.38067536 0.000716478 CECR1 1.44192153 0.000724918 BBS2 1.40792967 0.000760902 TBC1D16 1.36274032 0.000767741 PLCB4 1.42820241 0.00078212 C6orf226 1.32994109 0.000790244 NEK8 1.43237664 0.000797572 CASZ1 1.32519669 0.000798227 FAM83F 1.30387891 0.000803175 FAM50B 1.45773877 0.000804254 MED25 1.42685339 0.000826485 PYCRL 1.40030647 0.00084076 PDXP 1.46783132 0.000841656 EXOSC6 1.34741976 0.000856333 VSTM2L 1.92924479 0.000864429 SLC25A29 1.30866247 0.000882489 APOD 1.86608903 0.000889037 LOC728743 1.75169318 0.00089053 ZNF628 1.42007237 0.000892028 COBL 1.40319221 0.000896699 TTC30A 1.67935463 0.000904764 RAB40C 1.32476452 0.000914679 WDR92 1.46789585 0.000918523 BBS12 1.49170368 0.000920472 SCAF1 1.27078484 0.000920472 EXD3 1.63736942 0.000922835 C16orf42 1.26458944 0.000924002 CBX7 1.30724875 0.000931098 KLHL29 1.52045452 0.000934632 MTA1 1.28935596 0.000934937 ZNF496 1.38327158 0.000955848 ANKRD45 1.70738389 0.000963023 LOC388564 1.93649556 0.000967111 HAGH 1.32213624 0.000998155 PDGFA 1.42863088 0.001019324 ZFP3 1.42226786 0.001019324 ST5 1.34063535 0.001032342 SLC39A13 1.36833179 0.001039645 XYLT2 1.32074435 0.001043171 OGFOD2 1.37705326 0.001063251 CCDC106 1.38920751 0.001077622 C10orf57 1.39625227 0.00108256 TYSND1 1.32704457 0.00108435 ZNF428 1.25531565 0.001085719 ZBTB7A 1.27318182 0.001101095 FLJ90757 1.41213053 0.001112519 TMEM120B 1.35883101 0.001112519 KIAA1456 1.49996729 0.001115207 FAM125B 1.40872274 0.001117603 CLSTN1 1.3290101 0.001119504 SF3A2 1.28509238 0.001134443 DYNC2LI1 1.43389873 0.00114729 SIGIRR 1.28806752 0.00114729 ABHD14B 1.32342281 0.001156608 OSBPL5 1.35005294 0.001181561 GCDH 1.32866052 0.001181561 GLTSCR1 1.31492951 0.001183371 TMEM175 1.31373498 0.001185533 TRAPPC6A 1.3224038 0.001185954 HSD11B2 1.48148593 0.001191262 DEXI 1.28219144 0.001199474 TCF7 1.40542673 0.001215045 B4GALT7 1.28277814 0.001225929 MYBBP1A 1.34519608 0.00122885 ATXN7L1 1.41659202 0.001242233 PIN1 1.30404482 0.001254241 MT2A 2.04000703 0.001255227 DNAJB2 1.28234552 0.001261961 EPN1 1.26463544 0.001280015 TMEM61 1.50446719 0.001281574 C7orf47 1.27854479 0.001321603 IDUA 1.37272518 0.001349843 MACROD1 1.33230567 0.001350085 SERPINB10 1.94661954 0.001361514 ADCK3 1.28015615 0.001363257 CD99L2 1.37191778 0.001364491 SIVA1 1.26797988 0.001374975 ST6GALNAC6 1.31105149 0.001381949 KIAA0284 1.30334689 0.001396666 DNASE1L1 1.29767606 0.001422038 BPHL 1.35364961 0.001457025 KCTD17 1.41885194 0.001460503 REXO1 1.27951422 0.001466253 PLEFCHA4 1.5120144 0.001477764 LOC202781 1.39766879 0.001490088 ZCWPW1 1.4170765 0.001527816 BPIFB1 1.57081973 0.001561587 LRRC68 1.31705305 0.00159354 PITPNM3 1.30084505 0.00159354 TTC22 1.29235387 0.00159354 IRF2BP1 1.28392082 0.00159354 C11orf92 1.50310038 0.001602954 PPP2R3B 1.33531577 0.001643944 GALNTL4 1.32355512 0.001671166 NFIC 1.31815493 0.001671166 SELO 1.29376914 0.001682582 GPX4 1.30577473 0.001695128 CYP2J2 1.3244996 0.001696726 LHPP 1.2977942 0.001696726 DNLZ 1.45201735 0.001710038 DGCR6L 1.28160338 0.00171044 GATS 1.34306522 0.001752534 NAF1 1.46514246 0.001758144 PAK4 1.32518993 0.001765767 TMEM138 1.3805845 0.001773926 D2HGDH 1.31785815 0.001788379 NR2F2 1.33842839 0.001803287 EPB49 1.32650369 0.001819396 POFUT2 1.31411257 0.001820415 B3GAT3 1.35107174 0.001832824 GLI4 1.44684606 0.001837393 FGF11 1.39446213 0.001840765 RHBDD2 1.26141125 0.001840765 ZNF444 1.3510369 0.001852547 PEBP1 1.30689705 0.001854974 ZCCHC3 1.34025699 0.001863781 LRRC37A4 1.4519284 0.001865 TUBGCP6 1.30193887 0.001904076 XRCC3 1.3864244 0.001922788 RNF187 1.29592471 0.001936892 NCRNA00265 1.3750193 0.001948591 WRB 1.40277381 0.001971203 CHST14 1.38178684 0.001993182 PIK3R2 1.30114605 0.002023385 UBTD1 1.28646654 0.002023385 SEC14L5 1.76950735 0.00203473 SFI1 1.34394937 0.002037678 DPY30 1.32184041 0.002046145 HSF1 1.31711734 0.002053899 NME4 1.30387104 0.002071504 RBM43 1.40951659 0.002083034 FAM98C 1.274507 0.002089047 EML2 1.32629448 0.002117113 ZNF219 1.29662551 0.002118188 C20orf194 1.37210455 0.002121672 B4GALNT3 1.30834896 0.002163609 OBSL1 1.305937 0.00217526 C18orf10 1.32144956 0.002179978 NAGLU 1.27039068 0.002183662 MUC2 2.27000647 0.002193863 MGLL 1.27904425 0.002205765 FAM173A 1.38467098 0.002209168 PSIP1 1.34684146 0.002212642 TSPAN1 1.27665824 0.002224043 TUSC2 1.29490502 0.002232434 PROM1 1.46799121 0.002239807 POLD2 1.31983997 0.002243731 SCRIB 1.29183479 0.002243731 JMJD8 1.24988195 0.002286644 RBP1 1.29553455 0.002297925 UTRN 1.35691111 0.002362252 PARP3 1.34735994 0.002369225 RASSF6 1.39490614 0.002390815 LOC92249 1.40466136 0.002391912 OVCA2 1.3163436 0.002404409 TRIM56 1.29535959 0.002427233 TREX1 1.26637345 0.002431847 PECR 1.38681797 0.002480649 FBXL14 1.33944092 0.002480649 TCN2 1.28764878 0.002480649 THOC3 1.35544993 0.002495975 MRPL41 1.4462408 0.002497021 WNT3A 1.56505668 0.002502772 MAP1LC3A 1.35719631 0.002502772 TOP1MT 1.4172985 0.00251409 KREMEN1 1.24654847 0.00251866 LOC729013 1.39863494 0.002528217 TTLL1 1.43077672 0.002625335 DMPK 1.32867357 0.002625335 ODF2L 1.34583296 0.002626872 RBM20 1.43070108 0.00266198 CDC42EP5 1.49582876 0.002673583 ZNF608 1.40853604 0.002676791 EYA1 1.3918948 0.002677512 SLFN11 1.6901633 0.002694402 TMEM129 1.29584257 0.002694402 PEX14 1.32225002 0.002740151 MAPK8IP3 1.26167122 0.002782515 CDC20B 2.92979203 0.002783456 ROGDI 1.30155263 0.00278416 ABCB6 1.28553394 0.002829302 NEK1 1.48582987 0.002837851 TIGD5 1.32981321 0.002841309 PNMA1 1.34478941 0.002879762 MLXIP 1.29784865 0.002879762 SHANK3 1.49177371 0.002905903 STEAP3 1.30957029 0.002908485 CUTA 1.27360936 0.002926573 FOXK1 1.28002126 0.002930286 MFSD7 1.25269625 0.002962728 LONRF2 1.51428834 0.003024428 TRIT1 1.41931182 0.003031643 MFI2 1.33497681 0.003031643 CYP4B1 1.5268612 0.003087739 CIT 1.29305217 0.003090804 C8orf82 1.31308077 0.00315658 PTPMT1 1.28651139 0.003168897 SPHK2 1.30201644 0.003181927 TTC7A 1.28286232 0.003226858 CLCN4 1.36981571 0.003255752 MSI2 1.35012032 0.003301438 ING5 1.41166882 0.003322367 PFN2 1.3345102 0.003361105 SGSM1 1.48304522 0.00338494 DUSP28 1.40424776 0.003417564 MGMT 1.28389471 0.003429868 TP63 1.59679744 0.003467929 BTBD9 1.31826402 0.003467929 IL17RC 1.24675615 0.003467929 ODZ4 1.36904786 0.003524126 ZNF395 1.29186035 0.003586842 YDJC 1.33057894 0.003598986 APOO 1.34408585 0.003608735 SVEP1 1.40836202 0.003638829 RAB11FIP3 1.3058731 0.003671701 TEF 1.3271192 0.003677553 PIGQ 1.2693317 0.003740448 LGALS9B 1.36354436 0.003783693 MAOB 1.66197193 0.003808831 EID2 1.27884537 0.003835751 BAD 1.25388842 0.003897732 BTBD2 1.3199268 0.003913864 WNT5B 1.43246867 0.003931223 SLC25A10 1.24603921 0.004010737 PLK4 1.81340223 0.004056611 CEP97 1.41538101 0.004071998 FAM53B 1.26253686 0.00411007 CTSF 1.3223521 0.004131025 C9orf86 1.2153444 0.004156197 MAST2 1.32022199 0.004165643 TSKU 1.29264907 0.004165643 CTBP1 1.2796825 0.004188226 CES2 1.2809789 0.00419032 ZNF747 1.35584614 0.004211769 LOC100129034 1.27756324 0.004253091 HIST3H2A 1.37492639 0.0043908 C16orf13 1.2824815 0.00441089 ITGB4 1.28611762 0.004452134 MED24 1.28423462 0.004500601 IYD 1.44205522 0.004540332 C2orf54 1.30578019 0.004584237 PRRC2B 1.28521665 0.004638924 PHF7 1.38040111 0.004645863 MFSD3 1.25286479 0.004724472 PARD6G 1.35223208 0.004755624 POC1A 1.58918583 0.00476711 LAMC2 1.33269517 0.004830864 RABEP2 1.23103314 0.004830864 HSPB11 1.30028439 0.004881315 LOC642361 1.32431188 0.004908329 LIME1 1.30504035 0.0049123 FLYWCH1 1.28311096 0.004926395 ANG 1.30320826 0.005082111 QTRT1 1.29616636 0.005082111 CMTM4 1.31610931 0.005122846 TMEM125 1.26660312 0.005185303 SLC22A18 1.25291574 0.005205062 KIAA1549 1.32573653 0.005215326 PRR5L 1.28471689 0.0052441 MOCS1 1.41983774 0.00527108 LIG3 1.36586625 0.005275193 CEP85 1.34134846 0.005281836 NGFR 2.00940868 0.005299414 FBXO27 1.30963588 0.005345999 B4GALT2 1.27095263 0.005369313 GRINA 1.22714784 0.005469662 HMGN3 1.30614416 0.005501463 SLC38A10 1.23802809 0.005603169 PTPRF 1.26953871 0.005666966 GBP6 1.48338148 0.005693169 BMP7 1.28713632 0.005693169 SAMD1 1.33223945 0.005760574 GLTPD2 1.38603298 0.005780154 WDPCP 1.43105126 0.005868184 ZNF764 1.32764703 0.005880763 SLC7A4 1.38094904 0.005896344 GRB10 1.24234552 0.005898053 PRICKLE3 1.3269405 0.005899727 CCDC61 1.31458986 0.005914279 LTK 1.32450408 0.005930841 ITM2C 1.25343875 0.005945917 TAB1 1.3138026 0.005986003 WDR5B 1.39199432 0.006027191 EVC 1.36532048 0.006041191 SLC39A3 1.2652111 0.006058887 NAA40 1.31875635 0.006126576 ZNF696 1.34935807 0.006126723 CCDC57 1.37984887 0.006169795 B3GNT1 1.34790314 0.006464002 SCNN1B 1.24287546 0.006510517 SAP30 1.37835625 0.00653315 FAM3A 1.21815206 0.006541067 CYP27A1 1.39178134 0.006574926 GMPPB 1.26122262 0.006743861 POLI 1.37956907 0.006792284 ALDH16A1 1.22035177 0.006837667 MSLN 1.33518432 0.006865695 WDTC1 1.24564439 0.006879974 RAB11B 1.23317496 0.006954255 HRASLS2 1.44393323 0.006995945 DAGLA 1.31649105 0.006995945 DCXR 1.23902542 0.007010789 PLEKHH1 1.29761579 0.007058065 NUDT16L1 1.24681519 0.007069306 KLHL26 1.35470062 0.007102702 NPIPL3 1.26640845 0.007118708 DUOX1 1.28208189 0.007150069 LTBP2 1.28195811 0.007190191 TCTA 1.30149363 0.007212297 SPR 1.28479279 0.007287193 ZFYVE28 1.39878951 0.007333848 AGPAT4 1.37723985 0.007347907 SLC39A11 1.27733497 0.007353196 TMEM150C 1.35301424 0.007388326 CDC42BPG 1.26124605 0.007488491 SLC7A1 1.28202511 0.007507941 COL4A5 1.32559521 0.007512488 PAX7 1.3155991 0.007535441 ISOC2 1.23948495 0.007577305 AGPAT3 1.26745455 0.007585223 USP31 1.35428511 0.007618314 PCSK5 1.29446783 0.007618314 SLC16A5 1.25930381 0.007670005 NOL3 1.2781252 0.00767895 FBXL8 1.43124805 0.007687014 SNRNP25 1.28739727 0.007722414 CDCA7L 1.34644696 0.007787269 MOSPD3 1.27745533 0.007817906 CACNB3 1.33319457 0.007881717 ACBD7 1.5826075 0.007886797 ADCY2 1.66275163 0.007889009 CGNL1 1.27908311 0.007934511 PLEKHH3 1.24634845 0.007946023 CNNM2 1.38525605 0.007983142 FIZ1 1.28867102 0.00798317 DNHD1 1.38047028 0.008084565 PHPT1 1.26190344 0.008084565 TSPYL5 1.36008323 0.008097033 IRX5 1.25420627 0.008212841 STK11IP 1.23490937 0.008220192 CHPF 1.27265262 0.00823526 STOX2 1.3946561 0.00826187 TTBK2 1.3997974 0.008275791 CBX8 1.36626331 0.008275791 PPP1R3F 1.32059699 0.008334819 JOSD2 1.48865236 0.008361772 C17orf59 1.28230989 0.008361772 DECR2 1.23796832 0.008455759 TMEM143 1.37235803 0.008476405 OPLAH 1.25881928 0.008476405 MYPOP 1.29609705 0.008483284 CEL 1.93651713 0.008531505 BCL2 1.39092608 0.00871498 NGEF 1.52005004 0.008775214 USP21 1.31913668 0.008780827 RAD9A 1.25389182 0.008780827 LGALS3BP 1.24961354 0.008801136 LGALS9C 1.43680372 0.008865252 UPF1 1.25440678 0.008873906 LEMD2 1.20960949 0.008877864 ZFP41 1.34143098 0.009044513 SEPN1 1.26474089 0.009084 PLLP 1.31604938 0.00913286 CUL7 1.27441781 0.009164349 KRBA1 1.27792781 0.00923669 FAM195B 1.21801424 0.009241888 ATG9B 1.43120177 0.009248504 ARHGEF17 1.30638434 0.009248504 NUAK1 1.2674662 0.009299617 ENDOV 1.39721558 0.009324361 SCARA3 1.32119045 0.009332766 LAMB1 1.50281672 0.009344234 CIDEB 1.28399596 0.009344234 KLHDC7A 1.30138188 0.009386153 WLS 1.23889735 0.009435274 FAM161B 1.36982011 0.009478536 PACS2 1.26997864 0.009508236 SLC25A23 1.26489355 0.009521659 FAM164A 1.50789785 0.009626128 C1orf110 1.3202239 0.00963096 CENPB 1.18615837 0.009652916 ZNF704 1.33301508 0.009690515 C19orf6 1.20316007 0.009730685 KIAA0753 1.30653182 0.009784699 CST3 1.21230246 0.009784699 SLC41A3 1.25668605 0.00979418 PEX10 1.27191387 0.009844346 C12orf76 1.42258291 0.009870686 SLC1A5 1.24890407 0.009910692 RAP1GAP 1.3443049 0.009932188 GRAMD1C 1.36938141 0.009956926 NME3 1.33160165 0.010064843 ABHD8 1.27046682 0.010270086 ANKS1A 1.28882538 0.010380221 SLC25A38 1.29944952 0.010501494 SERPINF2 1.3305424 0.010548835 TP53I13 1.32153864 0.010567211 PANX2 1.31303008 0.010589648 ALKBH5 1.25805436 0.010606283 CHST6 1.25428683 0.01060947 WDR83 1.31345803 0.010637404 SERPINB11 1.4704188 0.010638878 SIX5 1.33395042 0.01072225 KIAA0319 1.34703243 0.010736018 ABCC10 1.26473091 0.01082689 EPCAM 1.2567134 0.010932803 C15orf38 1.30075878 0.010969472 AXIN2 1.29402405 0.011001282 NISCH 1.25096394 0.011018413 IGF2BP2 1.30475867 0.011048991 MOSC2 1.47927047 0.011053117 KIAA1908 1.35564703 0.01110532 SESN1 1.31752072 0.011207697 C1orf86 1.28409107 0.011320516 G6PC3 1.2125164 0.011409549 B3GALT6 1.22733693 0.011440605 KIF3A 1.38292341 0.011569466 FMO5 1.38477766 0.011656611 FOXP2 1.37687706 0.011656611 EP400 1.28435344 0.011755788 CYP2S1 1.27545746 0.011755788 VEGFB 1.22471026 0.011755788 TRIM32 1.29368942 0.011769481 TSNARE1 1.3634355 0.011803378 LSM4 1.23306793 0.012045042 SAMHD1 1.35015325 0.01211293 GALT 1.33655074 0.012150017 CHST12 1.29296088 0.012150017 SUMF2 1.24339802 0.012170682 C14orf80 1.29511855 0.012344687 TFPI2 1.6495853 0.012357876 NUDT7 1.51871011 0.012357876 PNKP 1.24958927 0.012357876 PFKM 1.29401217 0.012409059 MDC1 1.29181732 0.012467682 C17orf108 1.32080282 0.012502986 MRPL4 1.22051577 0.012531908 CTTNBP2 1.34156692 0.012602161 NEK6 1.24934177 0.01272017 APCDD1 1.37290114 0.012767663 SNAPC1 1.31811966 0.012784092 CUL9 1.24321273 0.012798949 DCBLD2 1.29914309 0.012917806 CHID1 1.23513008 0.012952152 PELP1 1.19235772 0.012973503 IL2RB 1.87694069 0.012983156 EBPL 1.24533429 0.013071502 TMEM110 1.29864886 0.013215192 EGFR 1.28277513 0.013226151 ACAT1 1.27648584 0.013237073 FADD 1.22480421 0.013237073 NCOR2 1.24365674 0.013251736 DUSP23 1.18759129 0.0134367 MIPOL1 1.35481022 0.013580231 IFT52 1.32547528 0.013981771 FGGY 1.38422354 0.014047872 ACTR1B 1.24578421 0.014079645 TRIOBP 1.21105055 0.014166645 MTR 1.29454229 0.01416807 C16orf45 1.33701418 0.014182012 TECPR1 1.26017688 0.014209406 ZNF362 1.2501977 0.014247609 TMEM25 1.31255258 0.014250634 ATP13A1 1.21286134 0.0142645 ALDH4A1 1.29508866 0.014386525 GHDC 1.2679717 0.014585547 USP13 1.6468891 0.014645502 IQCB1 1.30311921 0.014724122 PRMT7 1.26823696 0.014724122 SORBS3 1.22860767 0.014731446 RASA3 1.47946487 0.014788674 WDR18 1.22894705 0.014815312 UBB 1.21302285 0.014959845 ZNF626 1.36143599 0.014974802 CCHCR1 1.25121215 0.01509939 C12orf10 1.22594687 0.015249346 RGS12 1.1884216 0.015281037 GGA2 1.23527724 0.015332188 C9orf21 1.34640634 0.015553398 GAS2L1 1.27610616 0.015568411 USP11 1.25199232 0.015568411 LAGE3 1.2733059 0.015599785 CHST10 1.36346099 0.015732751 C1orf35 1.25664328 0.015735658 CPSF1 1.20966706 0.015929418 GJD3 1.22729981 0.016081967 DLG5 1.23092203 0.01610673 FAM83E 1.21694985 0.016195244 TRIM41 1.23404295 0.016320404 TMEM213 1.41958146 0.016484036 POR 1.21138529 0.016499043 LOC642852 1.46862266 0.016517072 SDHAF1 1.24223826 0.016806901 SIAH2 1.21834713 0.016864416 ZNF532 1.28788883 0.017020986 PHF17 1.25357933 0.017175754 ZMYM3 1.30001737 0.0171865 OCEL1 1.28256237 0.0171865 RSG1 1.28718113 0.017273993 NPTXR 1.53025827 0.01727628 LONP1 1.20031058 0.017332363 GLT8D1 1.26957746 0.017460181 ORAI2 1.41328301 0.017490601 TIMM17B 1.19661829 0.017535321 HEXDC 1.25292301 0.017542776 UGT2A1 1.36534557 0.017548434 URB1 1.25831813 0.017553338 ARMC5 1.22604157 0.017553338 TFF3 2.31909088 0.017587024 ASPSCR1 1.20844515 0.017624999 MRPS26 1.23168805 0.017646918 TMEM134 1.2288306 0.017825679 STK11 1.17914687 0.017837909 XRRA1 1.39947437 0.017892419 PYROXD2 1.34484651 0.018019021 GNA11 1.25697334 0.018040997 AGRN 1.21988217 0.018182474 PDE4A 1.24320237 0.018184742 MSH3 1.29294165 0.018305998 DEGS2 1.28509551 0.018381891 L3MBTL2 1.25584577 0.018599944 C4orf14 1.26050592 0.018761187 ProSAPiP1 1.22530581 0.018761187 CTNNAL1 1.37868612 0.018768235 SGCB 1.36337998 0.018840796 NT5DC2 1.22263296 0.018877812 PHYHD1 1.27403407 0.018894874 ZNF768 1.26202922 0.018933778 TMEM109 1.23710661 0.019040413 VWA1 1.19869747 0.019040413 TM9SF1 1.24665895 0.019041146 CLPP 1.16917032 0.019115843 ROM1 1.26671873 0.019116421 ABHD6 1.29541914 0.019153377 WDR81 1.23318896 0.019364381 TBCB 1.24205622 0.019442997 IL27RA 1.33040297 0.019493867 LZTR1 1.26790326 0.019526164 KDELC2 1.30411719 0.01972224 CMBL 1.34033189 0.019737295 TMEM201 1.26474637 0.019843105 ANKS3 1.22989376 0.019990665 DENND1A 1.22638955 0.020155103 RGL1 1.24300802 0.020233871 ARFIGEF38 1.32067809 0.020237336 CD40 1.24570811 0.020269619 ALKBH7 1.26247813 0.020284142 SLC27A3 1.2354561 0.020421322 TMEM93 1.31673383 0.020430106 SIRT3 1.2475777 0.0205475 SLC25A14 1.36204426 0.020560099 IQCK 1.28636095 0.020640164 TCEANC2 1.28423081 0.020664899 COL21A1 1.50109849 0.020759278 RAB40B 1.25324034 0.020759278 TNS3 1.2532701 0.020795029 COL7A1 1.57647835 0.020944269 CEP120 1.31831944 0.021016979 MCM2 1.29689526 0.021126757 ABHD11 1.18994397 0.021329494 LOC399744 1.31540057 0.021430758 SLC22A23 1.24944619 0.021446138 ATP6V0C 1.17416259 0.021478528 C17orf61 1.26534127 0.021518422 MACROD2 1.37686707 0.021629967 LRP5 1.24470319 0.021949014 FBXL15 1.29192497 0.021972553 PTPRU 1.22543283 0.021972553 MUC15 1.3122479 0.02203807 MID1 1.27948316 0.022099398 HOOK2 1.24529255 0.022099398 CMAHP 1.21368898 0.022099398 SPRYD3 1.20858839 0.022099398 CEP78 1.33075635 0.022122696 FKBP11 1.26304562 0.022134566 DHCR7 1.25305322 0.022252456 PLOD3 1.25880788 0.022278867 SLC29A2 1.2646493 0.02232075 MAP3K14 1.21534306 0.022542624 TUBGCP2 1.20510805 0.022542624 C12orf74 1.26087188 0.022618056 C9orf103 1.35312494 0.022704588 ACSF2 1.24126062 0.022731424 DBP 1.21193124 0.022905376 SCMH1 1.30660024 0.023010481 DPYSL3 1.75851448 0.023022128 SLC25A1 1.19992302 0.023167199 H2AFX 1.21471359 0.023460117 ACO2 1.24219638 0.023491443 SETD1A 1.23864333 0.02358174 HIGD2A 1.19776928 0.02358174 TNC 1.50094825 0.023589815 ZNF653 1.28833815 0.023589815 SPG7 1.21091885 0.023768493 PCP4L1 1.22918723 0.02383071 IBA57 1.24180643 0.023836751 C17orf101 1.25096951 0.023840587 MICALL2 1.22125277 0.024144748 SLC25A6 1.18752058 0.024216742 HLF 1.35897608 0.024265873 LDHD 1.2236788 0.024265873 HIC1 1.32339144 0.02431121 CDAN1 1.2574241 0.024430835 BLVRB 1.19730184 0.024565321 FANCF 1.30835319 0.024591866 C21orf33 1.23065152 0.02463506 EPB41L2 1.26976906 0.024700064 RANBP1 1.23115634 0.024823686 NUCB2 1.23698305 0.02484779 NCKAP5L 1.2397669 0.024923181 ZBED1 1.21522185 0.024923181 KBTBD6 1.4316415 0.025051133 THADA 1.27276897 0.025121918 GLIS2 1.33309074 0.02512733 ZNF787 1.16942772 0.025159688 AES 1.16914969 0.025347775 C14orf169 1.25236913 0.025508325 CAPN10 1.20119334 0.02551561 CX3C11 2.03560065 0.02571443 TP53BP1 1.30144588 0.025752829 EEF2K 1.22751357 0.026121177 ZNF629 1.19878625 0.026179758 PTK7 1.26249033 0.026187159 CYB5R3 1.22279029 0.026187912 GSDMB 1.22615544 0.026402701 ECHDC2 1.17956917 0.026402701 GSDMD 1.22611348 0.026430687 RAB26 1.3029921 0.026534641 LFNG 1.27842536 0.02667787 SREBF2 1.22653731 0.027051285 DNAJC27 1.33234962 0.027090378 TMEM178 1.32401023 0.027240857 IVD 1.24553409 0.027240857 PEMT 1.2385554 0.02725035 HIST2H2BF 1.25568147 0.027417938 TNRC18 1.20092173 0.027612815 PPP5C 1.25860277 0.027781088 AHSA2 1.33551621 0.027828419 FAM171A1 1.2547829 0.027880091 CYP2B6 1.89206892 0.02801745 QSOX2 1.30285256 0.0282336 SCD5 1.24820591 0.0282336 CEP164 1.25975237 0.028265449 RPL13 1.19710205 0.028278399 BANF1 1.22270928 0.02848803 ZNF777 1.22715757 0.028513321 EPHX1 1.19634133 0.028554468 TRPM4 1.19491647 0.028592325 KIFAP3 1.32574468 0.028652927 SULT1A1 1.35803402 0.028720872 C1QBP 1.2250998 0.028744187 SH2B1 1.23275523 0.028748064 CYP2B7P1 1.3709621 0.029004147 CMIP 1.18939283 0.029028829 SLC2A11 1.34050851 0.029279513 SMG6 1.2413887 0.029305629 ARL2 1.23879567 0.029305629 TTC7B 1.41937755 0.029317704 CTDP1 1.16949182 0.029509238 LOXL1 1.29289943 0.02952562 CDS1 1.24920822 0.030016095 BOD1 1.24305642 0.030061948 PTPRS 1.25084066 0.030069163 ARHGEF19 1.23306546 0.030316941 PPAP2C 1.19053642 0.030316941 TRAF3 1.23277663 0.030350579 ZNF707 1.23412475 0.030818439 DIS3L 1.25442333 0.031179257 GGA1 1.19942103 0.031209924 SNTB1 1.23919253 0.031230312 KCTD13 1.22015811 0.031269564 SOX21 1.25686272 0.031295938 SLC9A3R1 1.19749434 0.031709604 GLTPD1 1.19038361 0.031717891 WTIP 1.26447786 0.031869682 RHOBTB2 1.26176919 0.032458791 POLRMT 1.19980497 0.032991066 SERTAD4 1.28870378 0.033069887 MPST 1.16862519 0.033104411 ZNRF3 1.34876959 0.033173043 P4HA2 1.25705664 0.033701888 MPV17L 1.26662253 0.03402012 ARHGEF18 1.20479337 0.03402012 ZNF385A 1.17649674 0.034069213 DDAH1 1.28088496 0.034092835 MLLT6 1.20261495 0.0341598 CPNE2 1.21968246 0.034227225 MRPS31 1.27242786 0.034296798 DHODH 1.2852554 0.034427626 DIP2C 1.25542149 0.03464283 SUSD3 1.28440939 0.034683637 PRKAR1B 1.23530537 0.034768811 CIRBP 1.18770113 0.034785942 CSNK1G2 1.13123724 0.034785942 TCEAL1 1.28209383 0.035208866 IPO13 1.24220969 0.035208866 RCCD1 1.335678 0.035266459 SLC23A2 1.23369819 0.035486274 HSF2 1.24483768 0.035535946 COG1 1.21528079 0.035737318 ZNF607 1.28896111 0.035814809 ZNF473 1.30191148 0.03587568 PRPF6 1.1570728 0.035909989 SLC7A8 1.24579493 0.035915271 DMWD 1.26441363 0.036031824 C7orf55 1.20257164 0.036467386 LOC152217 1.19366436 0.036569637 TMEM223 1.22267466 0.036595833 HDAC11 1.2172885 0.03684229 AKT3 1.32799964 0.037008607 LMTK3 1.29813131 0.037095716 TRAPPC5 1.20831411 0.037095716 ITFG2 1.23730793 0.037115391 KJAA1161 1.22160862 0.037232096 TFAP4 1.39134809 0.037263881 MAP1S 1.17464502 0.037440506 CAPN9 1.39055066 0.037748465 COG8 1.2314403 0.038062365 UPF3A 1.24255729 0.038707203 XPNPEP3 1.29860558 0.038818491 MFSD10 1.17159262 0.038901436 CD8A 1.58747274 0.03893846 SLC25A22 1.24064395 0.039092773 PAQR8 1.29464418 0.039244293 HIRIP3 1.22398822 0.039367991 TRIM8 1.18882424 0.039367991 OAF 1.23071976 0.039512526 SNCA 1.27821293 0.040095856 8-Sep 1.18728437 0.040095856 C3 1.52927726 0.040833841 C17orf89 1.218819 0.041044444 TRIM28 1.18909519 0.041103346 CARD10 1.23773554 0.041297199 TMEM141 1.19110714 0.041365589 C11orf31 1.14760658 0.041444485 THTPA 1.2910393 0.041760045 VKORC1 1.18718687 0.041892204 SELENBP1 1.1721689 0.042289115 DOFIH 1.22434618 0.042312153 BSCL2 1.3183409 0.042641173 FAIM 1.27952766 0.042673939 ZNF503 1.19706599 0.042673939 RNPEP 1.2030262 0.042712204 GPR153 1.21365345 0.042737806 LOC147727 1.27577433 0.042987541 TMEM218 1.29964029 0.043031867 DDX51 1.2431896 0.043259718 NBEA 1.24270767 0.043259718 KIAA0754 1.33628562 0.043584142 P4HA1 1.27680255 0.043633316 NUMA1 1.18675348 0.044086191 TPRA1 1.18791628 0.044350632 DHRS11 1.25981602 0.04459514 TMEM216 1.23211237 0.04472713 SEZ6L2 1.23005246 0.04472713 AGTRAP 1.21322042 0.04472713 PTPLAD2 1.39497647 0.044903769 PTPRCAP 1.41832342 0.044929234 C19orf29 1.20477082 0.044969597 FAM83H 1.17895261 0.045287191 SP8 1.26481614 0.045370219 PLEKHG4 1.24585626 0.045638621 TMEM9 1.21047154 0.045968953 ANKRD11 1.20248177 0.04613435 PABPC4 1.19064568 0.046299186 ALKBH6 1.2014857 0.046508916 C19orf63 1.18088252 0.046519544 GIGYF1 1.17275338 0.046738543 ZNF574 1.23128612 0.046937115 SDF4 1.16627093 0.046954331 CAMK1 1.23284144 0.047106124 TTLL4 1.20520638 0.047538908 SULT1E1 1.4294267 0.047970508 RAB13 1.1740176 0.047981821 SMCR7 1.20475982 0.048036512 SCARB1 1.2307995 0.048174963 LCK 1.30353093 0.048431845 THBS3 1.1933001 0.048455354 NCDN 1.23307681 0.048579383 CAD 1.24055107 0.049142937 EEF2 1.18180291 0.049567914 DPH1 1.21637967 0.049735202 ASB1 1.21869366 0.049969351 NABA_CORE_MATRISOME Ensemble of   2.71E−07 genes Encoding core Extracellular matrix including ECM glycoproteins, collagens and proteoglycans NABA_ECM_GLYCOPROTEINS Genes encoding   8.91E−07 structural ECM glycoproteins REACTOME_RECRUITMENT_OF_MITOTIC_CENTROSOME_PROTEINS_AND Genes involved   2.86E−06 COMPLEXES in Recruitment of mitotic centrosome proteins and complexes REACTOME_MITOTIC_G2_G2_M_PHASES Genes involved   3.98E−05 in Mitotic G2- G2/M phases REACTOME_LOSS_OF_NLP_FROM_MITOTIC CENTROSOMES Genes involved   2.02E−04 in Loss of Nlp from mitotic centrosomes NABA_MATRISOME Ensemble of   2.10E−04 genes encoding extracellular matrix and extracellular matrix-associated proteins REACTOME_CHONDROITIN_SULFATE_DERMATAN_SULFATE_METABOLISM Genes involved   9.82E−04 in Chondroitin sulfate/dermatan sulfate metabolism REACTOME_METABOLISM_OF_LIPIDS_AND_LIPOPROTEINS Genes involved   9.82E−04 in Metabolism of lipids and lipoproteins KEGG_GLYCOSAMINOGLYCAN_BIOSYNTHESIS_CHONDROITIN_SULFATE Glycosaminoglycan   9.82E−04 biosynthesis - chondroitin sulfate REACTOME_GLYCOSAMINOGLYCAN_METABOLISM Genes involved   4.40E−03 in Glycosaminoglycan metabolism NABA_BASEMENT_MEMBRANES Genes encoding   7.36E−03 structural components of basement membranes REACTOME_DEVELOPMENTAL_BIOLOGY Genes involved   7.76E−03 in Developmental Biology REACTOME_AXON_GUIDANCE Genes involved   8.07E−03 in Axon guidance REACTOME_BIOLOGICAL_OXIDATIONS Genes involved   1.04E−02 in Biological oxidations REACTOME_CELL_CYCLE Genes involved   1.82E−02 in Cell Cycle KEGG_STEROID_BIOSYNTHESIS Steroid   1.85E−02 biosynthesis WNT_SIGNALING Genes related to   2.11E−02 Wnt-mediated signal transduction KEGG_PEROXISOME Peroxisome   2.78E−02 PID_INTEGRIN 1_PATHWAY Betal integrin   3.22E−02 cell surface interactions KEGG_ARGININE_AND_PROLINE_METABOLISM Arginine and   3.56E−02 proline metabolism REACTOME_SIGNALLING_BY_NGF Genes involved   4.13E−02 in Signalling by NGF REACTOME_TRANSMEMBRANE_TRANSPORT_OF_SMALL_MOLECULES Genes involved   4.23E−02 in Transmembrane transport of small molecules KEGG_FOCAL_ADHESION Focal adhesion   4.23E−02 REACTOME_COLLAGEN_FORMATION Genes involved   4.67E−02 in Collagen formation PID_ALPHA_SYNUCLEIN_PATHWAY Alpha-synuclein   4.67E−02 signaling NABA_CORE_MATRISOME Ensemble of   2.71E−07 genes Encoding core extracellular matrix including ECM glycoproteins, collagens and proteoglycans NABA_ECM_GLYCOPROTEINS GenesEncoding   8.91E−07 structural ECM glycoproteins REACTOME_RECRUITMENT_OF_MITOTIC_CENTROSOME_PROTEINS_AND Genes involved   2.86E−06 COMPLEXES in Recruitment of mitotic centrosome proteins and complexes

TABLE 2B Under-expressed Genes and Pathways Fold Fold Change/ Change/ Gene/Pathway Description FDR Gene/Pathway Description FDR FAM126A 0.47044321 2.57E−13 USP38 0.77604465 0.01002147 ABCA12 0.54776675 1.99E−12 LOC100131096 0.78878335 0.01014235 ESR1 0.46793656 7.85E−12 KPNA2 0.78234347 0.01021201 SPIN4 0.54280156 3.77E−10 DNTTIP2 0.77627102 0.01027009 PTER 0.59011532 4.29E−10 PPM1B 0.7741435 0.01027009 DYNLT3 0.58759988 2.06E−09 SLC19A2 0.77835972 0.01030816 LPAR6 0.59655276 2.28E−09 SLC43A3 0.74285594 0.01032916 KYNU 0.58810126 2.32E−09 TMCC3 0.4048631 0.01039145 DUSP10 0.52934498 3.08E−09 RAD21 0.79068443 0.01042223 ZDHHC21 0.60146742 5.22E−09 SLC30A7 0.79087734 0.01047273 POU2F3 0.51754048 1.01E−08 TCEB1 0.76866124 0.01050149 PRRG1 0.52569751 1.29E−08 PGM2L1 0.81470242 0.01050282 FAM40B 0.41827178 1.33E−08 ZNF207 0.78322085 0.01056721 RAB27B 0.63101586 1.81E−08 ZFC3H1 0.76322477 0.01058595 AGL 0.60797081 1.94E−08 MYOF 0.8174365 0.01072082 HS6ST2 0.50589265 4.17E−08 NEDD4 0.75183609 0.01072082 ERRFI1 0.59795439 5.59E−08 SYNJ1 0.74797515 0.01072082 MALL 0.60107268 6.80E−08 CHML 0.75999034 0.01073602 E2F2 0.54530533 9.00E−08 LYSMD3 0.81359844 0.01075889 ANKRD22 0.61522801 1.29E−07 XDH 0.7776994 0.01082657 MIER3 0.6186614 1.68E−07 STAG2 0.77433017 0.01089059 LOC100505839 0.54012654 1.86E−07 RGS1 0.428437 0.01099508 LHFPL2 0.6290898 1.89E−07 TINAGL1 0.76940891 0.01099801 PPARG 0.61457594 1.99E−07 PEX13 0.79652854 0.0110079 TMEM106B 0.62973645 2.17E−07 KRT6B 0.47469479 0.0110079 NRIP1 0.64071414 2.19E−07 C7orf60 0.72826754 0.01101626 TM4SF1 0.54686638 2.20E−07 ATP7A 0.78923096 0.01104899 PLK2 0.62474305 3.09E−07 UBTD2 0.78150066 0.01107608 C8orf4 0.5985907 3.40E−07 FGD4 0.76292428 0.01114875 MBOAT2 0.65711393 3.64E−07 HNRNPH3 0.78989996 0.01119847 TMPRSS11A 0.50012157 3.90E−07 GNPNAT1 0.80178069 0.01120254 HPSE 0.63345701 4.27E−07 SERPINB7 0.59831614 0.01120254 SP6 0.50873861 4.58E−07 TARS 0.787516 0.01122418 MCTP1 0.54747859 4.82E−07 UBLCP1 0.7722069 0.01122648 ECT2 0.65574576 6.32E−07 GARS 0.79199425 0.01132108 CYR61 0.56382112 6.47E−07 TMEM2 0.80301179 0.01138085 CFL2 0.62040497 6.48E−07 ZNF185 0.79182935 0.01143669 SLC18A2 0.6252582 6.95E−07 GDPD3 0.67570566 0.01143669 OCLN 0.66000035 6.98E−07 C5orf43 0.79637974 0.01148042 F2RL1 0.65645045 7.34E−07 SIRT1 0.74221538 0.01148042 OXSR1 0.6328292 7.42E−07 MAB21L3 0.77571866 0.01156947 DKK1 0.43751201 8.08E−07 LYRM5 0.76896782 0.01156947 LDHA 0.6605144 8.88E−07 IER3IP1 0.79267292 0.01158028 FABP5 0.59566267 1.03E−06 VEGFA 0.75291474 0.0116188 SLC38A2 0.65822916 1.05E−06 TMSB4X 0.72244795 0.01165661 PDP1 0.66035671 1.06E−06 TMEM41A 0.77944137 0.01168994 RND3 0.65234528 1.06E−06 TNFAIP3 0.65538935 0.01172668 CDKN2B 0.60249001 1.08E−06 INTS6 0.76205092 0.01172886 SERPINB5 0.56356085 1.19E−06 ADAM10 0.80151014 0.01175579 GPNMB 0.60704771 1.36E−06 ARAP2 0.7953511 0.0118699 HSD17B3 0.60203529 1.60E−06 CNN3 0.80690311 0.01188901 SERPINE2 0.34777028 1.62E−06 SPTY2D1 0.77603059 0.01194061 BZW1 0.67135675 1.72E−06 PHF20L1 0.77584582 0.01195426 MYEOV 0.49219284 1.72E−06 SERPINB1 0.61773856 0.01198815 SGK1 0.68010617 1.95E−06 HOMER1 0.75406296 0.01202166 DNAJB9 0.66020909 2.02E−06 PTK6 0.78404191 0.01213403 CALB1 0.31335579 2.19E−06 CAMSAP1L1 0.78125047 0.01215002 MSR1 0.49696801 2.44E−06 RNF11 0.78944171 0.01221391 C12orf29 0.63475403 2.52E−06 PPFIBP1 0.79937047 0.01235788 PLA2G7 0.44181773 2.68E−06 RP2 0.65113711 0.01246432 CAPZA2 0.63650318 3.06E−06 LTN1 0.81447306 0.01248787 CD109 0.56416931 3.06E−06 PAK1IP1 0.79300898 0.01253176 RAPH1 0.69473071 3.27E−06 ZNF189 0.76756049 0.01260727 CERS3 0.63914564 3.33E−06 BZW2 0.79754386 0.01273528 ETV4 0.59884423 3.74E−06 PKP1 0.71932402 0.01278409 FOXN2 0.62642545 3.75E−06 ATF1 0.80930096 0.01279478 RPS6KA3 0.67623565 4.20E−06 LIN7C 0.79913296 0.01285667 BCL10 0.65894446 4.20E−06 S100A16 0.77701197 0.01291573 SLC5A3 0.53006887 4.63E−06 C1orf52 0.74541456 0.01291781 STK38L 0.62733421 4.91E−06 MYO5A 0.73515052 0.01297751 SNX16 0.63704107 5.31E−06 DEPTOR 0.79024652 0.01303209 STRN 0.67981453 5.81E−06 BAZ2B 0.7897409 0.0130574 HSPC159 0.6455435 6.64E−06 MEI 0.78969952 0.01306743 SLCO1B3 0.49485284 6.90E−06 NR4A2 0.70149781 0.01312925 SACS 0.62971335 7.24E−06 ASNSD1 0.79830294 0.01315637 PLIN2 0.62600964 7.25E−06 CATSPERB 0.70538226 0.01315637 HSPA13 0.64757842 7.51E−06 FRMD4B 0.7805225 0.01321553 DDX3X 0.67297758 8.43E−06 ZNF552 0.79768046 0.01346424 SDR16C5 0.67434136 8.57E−06 MFN1 0.81509879 0.01359256 AMD1 0.67760181 8.91E−06 USO1 0.80330724 0.01359256 ITGB8 0.67887254 9.95E−06 BPGM 0.78515609 0.01359256 SLC4A7 0.65708728 1.04E−05 CXCL2 0.39887063 0.01359787 PTP4A1 0.68607621 1.05E−05 PPP1CC 0.80893126 0.01365976 HNMT 0.68400423 1.05E−05 PCNP 0.79622567 0.01368486 PGM2 0.6609215 1.09E−05 S100A11 0.74267291 0.0136932 FCHO2 0.68699512 1.19E−05 ID2 0.75318731 0.0137174 OAS1 0.63160242 1.20E−05 IFRD1 0.42135251 0.0137174 MAPK6 0.684135 1.20E−05 SCFD1 0.80529038 0.01373021 GRAMD3 0.68353459 1.26E−05 EMP1 0.60588308 0.01373021 ABCA1 0.54787448 1.28E−05 LANCL3 0.68348747 0.01375217 SYTL5 0.70638291 1.28E−05 UBA6 0.79888098 0.01379958 GULP1 0.65824402 1.32E−05 RARS 0.79366989 0.0138429 PHLDA1 0.54172105 1.32E−05 C7orf73 0.76317263 0.01389162 NRIP3 0.60674778 1.35E−05 LCOR 0.81117554 0.01389191 UGT1A10 0.60272574 1.45E−05 PTPN12 0.60299739 0.01394062 TMED7 0.70617128 1.57E−05 IREB2 0.80814458 0.01401875 ZFAND6 0.67093358 1.57E−05 MACC1 0.80002988 0.01406745 CSTA 0.52443912 1.61E−05 B4GALT5 0.79715598 0.0141339 POF1B 0.69756087 1.69E−05 NAPEPLD 0.80214979 0.01416807 CLCA2 0.56020532 1.70E−05 HECA 0.72312723 0.01416807 CYP2E1 0.46030235 1.83E−05 SCEL 0.59978505 0.01427161 GPR115 0.51236684 1.94E−05 CDK19 0.75633313 0.01433637 STXBP5 0.68639477 1.95E−05 SOCS5 0.78388345 0.01441385 FHL2 0.69498993 2.13E−05 DGKA 0.78636133 0.01447758 EFNB2 0.68000514 2.13E−05 EIF3J 0.80032433 0.01469173 SPRY4 0.57593365 2.18E−05 MAP1LC3B 0.73616097 0.01472412 FRMD6 0.67585426 2.19E−05 IVL 0.51954316 0.01487199 SOX9 0.69148494 2.34E−05 SLC38A9 0.78548034 0.01488644 LYPLA1 0.68419869 2.40E−05 TXNDC9 0.80599778 0.01499161 SLC37A2 0.6397126 2.54E−05 ARFIGAP29 0.79975551 0.01502574 SLC6A14 0.63108881 2.66E−05 CHMP1B 0.78649063 0.01506495 TCN1 0.63504893 2.67E−05 CREB1 0.75968742 0.01506947 STS 0.71630909 2.67E−05 AURKA 0.7291468 0.01525634 CLDN1 0.71508575 2.70E−05 DENND1B 0.78917281 0.01528104 TGFB2 0.70221517 2.86E−05 SP3 0.80275018 0.01547056 PPP1CB 0.69356726 2.96E−05 ABCC9 0.75019099 0.01563394 COPS2 0.70745288 3.20E−05 LARP4 0.81575794 0.01573566 FNDC3B 0.70629744 3.27E−05 PSTPIP2 0.74759876 0.01576062 SLC9A2 0.70240663 3.45E−05 UBAP1 0.72271205 0.01576062 AHR 0.72189199 3.48E−05 GYG1 0.77805963 0.01581091 CPM 0.60903324 3.65E−05 KIAA1199 0.54860664 0.01593278 MRPS6 0.67128208 3.65E−05 SNRPB2 0.80292457 0.01593921 MAL2 0.71451061 4.09E−05 FBXO34 0.80748644 0.01598506 SLC9A4 0.68487854 4.09E−05 NFAT5 0.80662528 0.01610673 PLAU 0.60117497 4.14E−05 PURB 0.80015013 0.01638623 KCTD9 0.68717984 4.21E−05 VTA1 0.795135 0.01638623 CYP2C18 0.67036117 4.25E−05 ZBTB38 0.80217977 0.01644708 ARHGAP5 0.72532517 4.26E−05 CYB5R2 0.77288599 0.01648404 TDG 0.7023444 4.31E−05 EXOC5 0.81382561 0.01655428 RALA 0.68246265 4.39E−05 CDR2L 0.81728606 0.01659833 ANKDD1A 0.59706849 4.44E−05 SWAP70 0.80565394 0.0167099 CEACAM1 0.60936113 4.61E−05 GLRX3 0.78569526 0.0167132 TRPS1 0.68207878 4.80E−05 MMP7 0.51970705 0.01674324 GALNT5 0.70688281 4.90E−05 C18orf19 0.80580272 0.0167524 AGPAT9 0.54621966 5.57E−05 IPPK 0.76399847 0.01679915 PLS1 0.73068821 5.63E−05 BLOC1S2 0.76302982 0.01685077 ABHD5 0.63310304 5.75E−05 PDLIM2 0.73531533 0.01685769 SLK 0.70996449 5.86E−05 OTUD6B 0.74806056 0.01696167 GNAI3 0.63637676 5.88E−05 POLR2K 0.78945634 0.01701766 GPCPD1 0.60712726 6.03E−05 C10orf118 0.81187016 0.01703642 FAT1 0.71499305 6.16E−05 RELL1 0.71318764 0.01707764 CAPZA1 0.69202454 6.43E−05 GLA 0.60796251 0.01727628 TUBB3 0.46563825 6.48E−05 PLXDC2 0.53165839 0.01733236 DSG3 0.44745628 6.87E−05 L3MBTL3 0.77911939 0.01735666 C6orf211 0.70372086 6.91E−05 RUNX2 0.77801083 0.01735666 SLMO2 0.70233453 7.10E−05 CA2 0.4922131 0.01735666 LOC100507127 0.44153481 7.20E−05 PPP4R2 0.79532914 0.01736433 MGAT4A 0.70002166 7.36E−05 LRRC8C 0.67202997 0.01753532 MST4 0.6716609 7.59E−05 ARID4B 0.77340187 0.01754278 UCA1 0.38849742 7.77E−05 SH3BGRL2 0.81075514 0.01755334 TPM4 0.69490548 7.82E−05 CPD 0.79596928 0.01755334 TBC1D23 0.70081911 8.08E−05 DNAJB6 0.78602264 0.01755334 C9orf150 0.65660789 8.16E−05 RG9MTD1 0.78287275 0.01755334 MPZL2 0.72416465 8.45E−05 TXN 0.77853577 0.01761555 BCAT1 0.60155977 8.50E−05 UGCG 0.81279199 0.01783791 PRRG4 0.69994187 8.66E−05 ARNTL 0.7595337 0.01792236 ANKRD57 0.69957309 8.92E−05 PRSS16 0.78421252 0.01793552 DSEL 0.66917039 8.92E−05 RAP2A 0.78860475 0.01801902 CCNC 0.72104813 9.50E−05 VAMP7 0.78098348 0.01804468 FGFBP1 0.55896463 9.83E−05 JOSD1 0.66714848 0.01818247 HEPH 0.63099648 0.00010094 TNFRSF12A 0.7674609 0.01827299 TIAM1 0.68576937 0.00010103 EXOC1 0.80533345 0.018306 FAR1 0.71009803 0.00010236 ACOX1 0.77467238 0.01836883 MANSC1 0.67745897 0.00010243 IQGAP1 0.78700289 0.01837327 TET2 0.69755723 0.00010428 PFKFB2 0.79393361 0.01838189 PTPN13 0.72165544 0.00010468 ID1 0.7077695 0.01838189 PLS3 0.70700001 0.0001063 ELMOD2 0.8099594 0.01839339 GRHL3 0.62055831 0.00011182 SSR3 0.8027967 0.01861183 TRIB2 0.70025116 0.00011358 A2M 0.7095884 0.01863194 VGLL1 0.66984802 0.00011809 PSMA3 0.80198438 0.01868687 HOOK3 0.71748877 0.00012006 TTC39B 0.78773869 0.01868687 FAM3C 0.71723806 0.00012006 SREK1IP1 0.78848537 0.01871407 BAZ1A 0.68508081 0.00012035 DNAJC25 0.7466337 0.01872135 CCDC88A 0.65999086 0.00012598 TPRKB 0.74502201 0.01872135 SPATA5 0.6904431 0.00012757 DCP2 0.69555649 0.01872135 SOCS6 0.71829579 0.00013007 MCU 0.80603403 0.01876119 TOB1 0.72241206 0.00013331 PVR 0.7660582 0.01876119 HIST1H2BK 0.66691073 0.00013571 ADRB2 0.75075306 0.01876119 TOP1 0.71883193 0.00013658 ATP13A3 0.82040209 0.0188408 SRPK1 0.69969324 0.00014184 ESRP1 0.80880005 0.0189173 LRIF1 0.69079735 0.00014297 TC2N 0.81169068 0.01891942 SPTSSA 0.7084399 0.00014301 ANXA3 0.80049136 0.01893378 RALGPS2 0.7046366 0.00014634 SPCS2 0.79971407 0.01893378 CHMP2B 0.70500108 0.00014894 CKS2 0.82098525 0.01900244 CXADR 0.72706834 0.00015072 SCOC 0.81832985 0.01902309 GSTA4 0.71794256 0.00015072 SGTB 0.63979487 0.01904115 NAA50 0.72321863 0.00015246 SYNM 0.73918101 0.01915338 SLC38A1 0.72718456 0.00015392 NETO2 0.74186068 0.01921827 GPRC5A 0.67982467 0.00015492 RAB1A 0.79371888 0.01931145 HRH1 0.57142076 0.00015553 DUSP4 0.7679591 0.01932028 SGPP1 0.60446113 0.00015983 TICAM1 0.71976999 0.01949387 DSC2 0.42009312 0.00016546 RBMXL1 0.77176321 0.01959763 REL 0.70232402 0.00016796 NIPAL1 0.75859871 0.01975244 SERPINB8 0.71948572 0.00017411 ARL15 0.78712448 0.01978067 ESRG 0.50616862 0.00017416 SPECC1 0.79037053 0.01997725 GMFB 0.71115128 0.00017772 RAET1G 0.76619179 0.01997725 CYCS 0.73195986 0.00017997 KLF5 0.81561175 0.01999447 ATP1B3 0.72625915 0.00018351 IFNAR1 0.76951871 0.02007723 SCYL2 0.72159083 0.00018351 USP3 0.77565612 0.0201071 KRAS 0.73375761 0.00018545 FAM83C 0.70142413 0.0201071 ZNF518B 0.6968451 0.00019734 TRIM16 0.81115941 0.0201551 PNPLA8 0.63204178 0.00020809 NR3C1 0.78608488 0.02017233 ASPH 0.72334386 0.00021314 CDC42SE2 0.78654377 0.02019726 LAMA4 0.60508669 0.00021337 CNIH4 0.76529362 0.02023387 PDE5A 0.62146953 0.00021406 SLC40A1 0.75686068 0.02023734 LY6D 0.52174522 0.00021584 METTL21D 0.72136719 0.02031329 SLC44A5 0.47103937 0.00023984 B3GNT5 0.73325211 0.02032869 XPO1 0.74477235 0.00024253 FZD5 0.81737971 0.02042132 SLC35F2 0.67225241 0.0002428 NUP50 0.81619664 0.02042132 SH2D1B 0.59115181 0.00024453 APC 0.79253541 0.02042132 MED13 0.71820172 0.00025206 OSMR 0.75202139 0.02042132 STXBP3 0.71330561 0.00025406 APOBEC3A 0.41742626 0.02042132 CTSL1 0.65567678 0.00025521 SLC10A7 0.78781367 0.02043964 CPEB4 0.70060068 0.00025668 DTX3L 0.80221646 0.02047647 FLVCR2 0.5867205 0.00026148 NR1D2 0.82110804 0.02059914 RNF141 0.72848197 0.00026362 ANXA2 0.81057352 0.02064016 RAB5A 0.71866507 0.00026829 BNIP3L 0.7921443 0.02065952 STEAP4 0.73753612 0.00027352 EEA1 0.82047062 0.02105772 NPC1 0.71394763 0.00027481 GLTP 0.79057504 0.0211003 ACTR3 0.67613118 0.00027918 ACAP2 0.79259531 0.02112664 SLC12A6 0.64629107 0.00028121 MXD1 0.40192887 0.02113344 TMEM167A 0.73039401 0.0002839 CALU 0.82233944 0.02117432 HBP1 0.71134346 0.00029684 PPP2R1B 0.82287537 0.02147113 GPR37 0.64413044 0.00030167 MANF 0.79019152 0.02147113 FAM135A 0.73205965 0.00030188 UBXN8 0.75092566 0.02147113 C12orf36 0.67818686 0.00030805 KRT13 0.5557856 0.02147113 CD58 0.62882881 0.00031182 CD55 0.7675448 0.02147853 MALAT1 0.35629204 0.00031256 PKP2 0.84172061 0.02150051 YWHAZ 0.7300418 0.0003126 PLAT 0.56494138 0.0215063 HBEGF 0.36825648 0.0003126 NEAT1 0.72062622 0.02173452 CLEC2B 0.41375232 0.00031403 NCOA3 0.81904203 0.02181149 CYB5R4 0.62282326 0.00031499 ZC3H12C 0.79419138 0.02181149 ATP10B 0.73014866 0.00032141 FAM49B 0.51183042 0.02209803 KCTD6 0.6982837 0.00032602 CUL4B 0.81000302 0.0220994 ITGA2 0.73729371 0.00032753 SCD 0.81856731 0.02225105 MGST1 0.74936959 0.00033476 FXYD5 0.61611839 0.02227887 CDRT1 0.6679511 0.00034261 C3orf58 0.7929907 0.02231832 SPRR1A 0.45298366 0.00034579 SOS2 0.78441202 0.02242783 UGT8 0.6364024 0.00036052 EPPK1 0.71847068 0.02247716 BIRC3 0.63931884 0.00036805 UBE4A 0.81949437 0.02247809 PAM 0.73943259 0.00036851 RLF 0.76493297 0.02249613 SMC4 0.72845839 0.00036886 MAGT1 0.81754733 0.02251014 ACTR2 0.7257177 0.00037179 DCTN6 0.79087132 0.02255614 RAB21 0.71063184 0.00038679 ITCH 0.81832417 0.02261806 SEC24A 0.74242518 0.00038918 TXNL1 0.80210696 0.02270459 ELL2 0.73642285 0.00039252 EPHA2 0.80043392 0.02270459 ARPC5 0.66218112 0.00039424 SLC10A5 0.75403621 0.02270459 PRDM1 0.56977817 0.00039519 CLEC7A 0.40086257 0.02273095 GK 0.56146426 0.00039726 ALG6 0.79281819 0.02273251 C14orf129 0.73022452 0.00040878 TMX3 0.82502213 0.02283395 CCDC99 0.72023731 0.00041286 RAB8B 0.51178041 0.02283395 PRSS3 0.42409665 0.00042522 ENPP4 0.82969342 0.02290538 USP25 0.71934778 0.00042769 SAMD4A 0.80115193 0.02290538 PKN2 0.71899998 0.00043042 GNG12 0.81800792 0.02290834 GPR87 0.73061781 0.00043214 MITF 0.79669058 0.02302213 RORA 0.70094713 0.00043625 UBE2J1 0.80232214 0.02305656 GGCT 0.7344833 0.00044515 KIAA1324L 0.84134374 0.02309417 ZNHIT6 0.76417154 0.00045036 TGFBR1 0.77759794 0.02324532 TMBIM1 0.72290834 0.00046454 CHM 0.82558253 0.02329511 TFPI 0.61640577 0.00048755 TMEM41B 0.80778275 0.02342002 BCAP29 0.72684992 0.00049294 JARID2 0.7674422 0.02350843 RCOR1 0.70144121 0.00049756 DYNC1LI1 0.79569175 0.02350861 LEO1 0.72295774 0.00051807 DNAJA1 0.80469715 0.0235662 OTUB2 0.6388429 0.00052599 CXCL3 0.57876868 0.0235662 TMPRSS11D 0.60003871 0.0005336 AFTPH 0.80550055 0.02358174 CP 0.73425817 0.000553 SCGB1A1 0.68088861 0.02358174 IKZF2 0.7513508 0.00055695 BMP3 0.81011626 0.02365337 ROD1 0.73886335 0.0005605 CCRL2 0.6009859 0.02365337 HPGD 0.74086493 0.00056145 SEL1L 0.82277025 0.0238405 NAPG 0.73799305 0.00056145 CASP7 0.81804453 0.0238405 RIT1 0.7194234 0.00056717 MED4 0.7939477 0.0238405 CLCA4 0.63982609 0.00059724 SLURP1 0.58553775 0.0238405 PPP3R1 0.70906132 0.00060194 C12orf4 0.82963799 0.02394378 GABPA 0.72611695 0.00060812 DENR 0.81434832 0.02394378 SPCS3 0.75238433 0.00061101 MKI67 0.65325272 0.02394378 ITGAV 0.74691451 0.00061101 CD84 0.70733746 0.02421674 LOC100289255 0.69618504 0.00061787 PGM3 0.82981262 0.02433953 ADAM9 0.75133718 0.00061987 VPS4B 0.81124865 0.02443084 FIIF1A 0.62106857 0.00061987 SLC7A11 0.7055667 0.02443084 GAN 0.67925484 0.00062053 CD44 0.77927941 0.02445288 EIF1AX 0.76260769 0.00062186 SLC1A1 0.75927386 0.02456729 WASL 0.74896466 0.00062186 CLPX 0.80928724 0.024572 UBE2W 0.64239921 0.00063811 MOSPD1 0.80026606 0.02459523 RCAN1 0.71096698 0.00064856 ZC3H15 0.80450651 0.02467764 SSR1 0.7514502 0.00065077 RABIIA 0.80437379 0.02482369 PHACTR2 0.75203507 0.00065103 DNAJB1 0.80659609 0.02483132 NCK1 0.73821734 0.00065616 SC5DL 0.81585449 0.02492318 SDS 0.43860257 0.00065851 PON2 0.79911935 0.02492318 ZNF460 0.6508334 0.00066048 WAC 0.80996863 0.02494557 SPAG9 0.7041979 0.00066393 IRAK2 0.78621119 0.02498706 ETFA 0.7376278 0.0006674 MAN2A1 0.80945847 0.02501316 TBL1XR1 0.77064376 0.00066959 NRP1 0.75842343 0.02501316 MET 0.75295132 0.00066959 NFKBIA 0.64409994 0.02509502 LOC100499177 0.6435527 0.00066959 ZNF143 0.78375832 0.02519086 RC3H1 0.71187912 0.00067619 OSTC 0.81380824 0.02520621 PPP1R15B 0.72604754 0.000685 DHX15 0.80218767 0.0252546 RBMS1 0.72833819 0.00069497 USP32 0.69625972 0.02547673 PAPSS2 0.73311321 0.00070388 CMAS 0.80689954 0.02563124 FGFR1OP2 0.72583355 0.00070539 ATP6V1G1 0.79750807 0.02563124 PHF6 0.74176092 0.00071648 ARPC3 0.74025507 0.02567149 RAB27A 0.69715587 0.00072005 PTAR1 0.82246466 0.02577645 MAP4K4 0.69994514 0.00072785 ABCE1 0.8206001 0.02577645 PRKAR2B 0.7353908 0.00074015 ZNF260 0.81726679 0.02577645 ANXA1 0.73823795 0.00074408 VNN1 0.47957675 0.02591115 LOC100134229 0.73183087 0.00074435 TPM3 0.77578302 0.02596422 OSTM1 0.71670885 0.00075171 CNNM1 0.75796579 0.02596422 SMOX 0.59247896 0.00075968 MED21 0.78624253 0.02601824 RTKN2 0.67259731 0.00076669 GM2A 0.80553342 0.02604295 TMEM64 0.751443 0.00076931 PSMC2 0.81330981 0.02617976 BRWD3 0.70874449 0.00077331 RAP1B 0.79847594 0.02618716 YTHDF3 0.73166588 0.00077638 CYP4X1 0.71483031 0.02618716 CLDN4 0.71007023 0.00077802 PHTF2 0.81641271 0.0262022 MMP1 0.55376446 0.00077869 UBE2V2 0.81033911 0.02626899 KCNN4 0.68465172 0.00079015 ARHGAP20 0.78890875 0.02632695 CLDN12 0.76454862 0.0007909 RHBDL2 0.79592484 0.0264027 COQ10B 0.71874588 0.00079995 SMAP1 0.81113172 0.02649101 LRP12 0.71964731 0.00080097 KRT10 0.68898712 0.02653464 FOSL1 0.51166802 0.00082386 RFK 0.80461614 0.02655103 PARD6B 0.74223837 0.00082622 RAP1GDS1 0.8420239 0.02657993 LOC439990 0.69267458 0.00083354 MAPK1IP1L 0.82200085 0.02658191 PDLIM5 0.76185114 0.00084129 SLC35A5 0.81757126 0.02659754 LTBP1 0.73928714 0.00084166 GDAP2 0.776095 0.02667787 HIGD1A 0.74108416 0.00084269 MIB1 0.82312043 0.02681784 RANBP6 0.72113191 0.00085429 ITPR2 0.72381288 0.02688482 AFF4 0.75419694 0.00086212 PGRMC2 0.82715791 0.02695215 RCBTB2 0.72276464 0.00088071 RAB14 0.8177047 0.02700102 DEFB1 0.56084482 0.00088306 ARL4A 0.82412052 0.02702553 SORBS1 0.69135874 0.00090133 RYBP 0.69095215 0.02702816 LACTB2 0.75713601 0.00092553 TDP2 0.68722637 0.02707132 DAB2 0.69448887 0.00092633 CBX3 0.80911237 0.02714575 ZNF431 0.70801523 0.00092668 TBC1D15 0.79826732 0.02725035 MAN1A1 0.74578309 0.00093774 ZNF292 0.79336479 0.02727831 RNF19A 0.7499563 0.00094857 DEK 0.79668216 0.02738693 SRD5A3 0.68412211 0.00094857 GTF2F2 0.79408033 0.0273958 SDCBP2 0.69112547 0.00096472 CCNG2 0.66348611 0.02746122 GLS 0.55743607 0.00096829 FBXW7 0.77030162 0.02750752 ARRDC3 0.73257404 0.00098514 NCOA7 0.67006969 0.02759494 PDZD8 0.74504511 0.00101932 SLC39A10 0.81569938 0.02762611 NT5C2 0.74411832 0.00102102 CXCL1 0.5037887 0.02773044 DDX52 0.74116607 0.00102436 LMBRD2 0.79862543 0.02773263 ZNF326 0.73410121 0.00104743 RNF139 0.77894417 0.0277779 SDCBP 0.51524162 0.00106089 ATXN3 0.81712764 0.02778695 TAB2 0.73583939 0.00106325 HMGCS1 0.83634026 0.02780334 MDFIC 0.75928971 0.00107939 GAB1 0.75314903 0.02799812 FAM126B 0.65824303 0.00109786 DR1 0.79711312 0.02810783 MAT2A 0.76256991 0.00110997 TJP1 0.815017 0.02814271 SAMD9 0.60678126 0.00110997 SSFA2 0.81751861 0.02821836 OSBPL8 0.69459764 0.00111029 SH3GLB1 0.80551167 0.02824311 LIG4 0.73079298 0.0011228 EDIL3 0.73606278 0.02837228 THRB 0.76151823 0.00114313 CMTM6 0.73956197 0.02838961 TNFRSF10D 0.62060304 0.00114435 PIK3C2A 0.83154276 0.02851279 RIOK3 0.73962901 0.00115102 PHACTR4 0.82152956 0.02867344 6-Mar 0.69528665 0.00117913 CD86 0.44546002 0.02875144 VPS26A 0.74010152 0.0012058 RSL24D1 0.80075639 0.02876288 GRHL1 0.74125467 0.00121284 MAP4K3 0.82252973 0.02880875 SEC23A 0.74746817 0.00122351 C4orf32 0.73140848 0.02889681 CLOCK 0.75080448 0.00124549 TGIF1 0.80327776 0.02900415 SAT1 0.70085873 0.00128002 NFYA 0.79091615 0.02900415 POLB 0.7265576 0.00129411 XRCC4 0.79014548 0.02906143 TAF13 0.74566967 0.00129461 BACH1 0.60345946 0.02933929 DSC3 0.67776861 0.00129939 PRPF18 0.79195926 0.02934951 SAMD8 0.73394378 0.00131822 HSPA5 0.82254051 0.02939332 NPEPPS 0.7437029 0.00132561 COBLL1 0.80869858 0.02939332 TPD52 0.75898328 0.00135933 STRN3 0.81460651 0.02940888 NCEH1 0.7474324 0.00136541 C16orf52 0.80347457 0.02940888 AP1S3 0.80504206 0.00136961 ACADSB 0.81872232 0.02951968 USP53 0.75319991 0.00137958 CLCF1 0.79372787 0.02959393 EDEM1 0.75561796 0.00139667 SBDS 0.82630688 0.02972834 MBNL1 0.74932328 0.00141178 C1orf96 0.73892616 0.02980835 TMEM33 0.74560237 0.00141178 SVIL 0.77354524 0.02993904 NMU 0.50565668 0.00141984 FRS2 0.82504155 0.02998364 CCPG1 0.74604118 0.0014299 DNAJB14 0.79384122 0.02998364 TBK1 0.73752066 0.00144402 IL8 0.12605808 0.02998364 PCMTD1 0.75791312 0.00146293 GJB4 0.79743165 0.03001609 SMNDC1 0.72111534 0.00147433 UBE2E1 0.8132693 0.03004003 ARNTL2 0.73486575 0.00151723 PRC1 0.76311242 0.03009422 CHPT1 0.72326837 0.00151723 KPNA4 0.79641384 0.03021352 SEC61G 0.7105942 0.00151723 ALDH3B2 0.80496463 0.03021519 SHISA2 0.59853622 0.00152782 ARFIP1 0.81639333 0.03031551 XIST 0.44631578 0.00155743 BMPR2 0.83541357 0.03031694 TMOD3 0.77533314 0.00157527 PUS10 0.73256187 0.03037422 HERC4 0.73058905 0.00159354 CENPN 0.76828791 0.03047261 FEM1C 0.76590656 0.00160833 YES1 0.82057502 0.03053073 TFRC 0.7570632 0.0016402 ZNF468 0.84177205 0.03072911 F8A1 0.7386134 0.00164374 PIK3CG 0.53271288 0.03078134 ATP1B1 0.76704609 0.0016534 LPCAT2 0.61892931 0.03081115 ZDHHC13 0.75504945 0.00166529 MAGOHB 0.77202271 0.03087813 ERV3.1 0.68654538 0.00167391 PGGT1B 0.81716901 0.03087848 TMEM30A 0.75615819 0.00169183 SIKE1 0.81047669 0.03087848 CCNYL1 0.74297343 0.00169817 C15orf52 0.7677753 0.03095296 IBTK 0.76516915 0.0017406 CHST4 0.75379626 0.03109953 KLF6 0.64386779 0.0017406 SLC28A3 0.80134905 0.03115551 MAP2K4 0.73093628 0.00175469 GTDC1 0.77009529 0.03131057 PICALM 0.60342183 0.00178068 ITPRIP 0.62964124 0.03136065 DCUN1D1 0.78777005 0.00178761 PERP 0.81957926 0.03145735 SRP19 0.73007773 0.00179995 PSMD5 0.81822219 0.03147226 GNE 0.76363264 0.00180792 CNIH 0.8396771 0.03158417 TMEM56 0.72176614 0.00184076 PDE4B 0.15925174 0.03166939 NUS1 0.76925969 0.00185255 FAM105A 0.76759455 0.03184924 TMED5 0.75920484 0.00185255 GABRE 0.72174883 0.03184924 PMAIP1 0.61359208 0.00185497 UHMK1 0.83795019 0.03186968 TM9SF3 0.76920471 0.00186378 CDK6 0.84259905 0.03206511 ARL8B 0.75277703 0.001865 GSPT1 0.81333116 0.03211789 CSTB 0.7246213 0.0018664 CLINT1 0.84129485 0.03258105 TAOK1 0.76340931 0.00187476 SPTLC1 0.82243139 0.03262099 FRK 0.74737271 0.00187862 OXR1 0.82634351 0.03273304 KRT6A 0.50297318 0.00188266 SYNCRIP 0.82737388 0.03294625 ZRANB2 0.73683865 0.00188671 TWSG1 0.82516604 0.03294625 MAOA 0.75804286 0.00190091 TUFT1 0.78129892 0.03294625 UBE2K 0.75499291 0.00193919 FAM98A 0.82227343 0.03311064 ZCCHC6 0.64117131 0.00197834 ANGPTL4 0.62447345 0.03316298 TACC1 0.73591479 0.00201604 SPIN1 0.82919111 0.03336936 TRAM1 0.76688878 0.00202235 FTSJD1 0.82751547 0.03348945 PNRC2 0.76237127 0.00202235 THBS1 0.3372848 0.03405027 CDC25B 0.73376831 0.00205757 YPEL2 0.83006226 0.03422723 MTHFD2 0.71278467 0.0020715 C1GALT1C1 0.82711113 0.03422723 ARL5B 0.65205708 0.00208123 SFT2D2 0.79342076 0.03422723 VBP1 0.7564177 0.00208303 NBPF14 0.62423931 0.03436711 IRS1 0.74430144 0.00209694 APPBP2 0.81820437 0.03439503 GALNT1 0.75884893 0.0021133 SUB1 0.79595423 0.03442763 CD68 0.69932459 0.0021133 CSTF2 0.81280844 0.03457978 ALDH1A1 0.78129241 0.00211381 SERPINB13 0.74386568 0.03462984 GALNT3 0.7706992 0.00216886 TAF12 0.75776079 0.03465156 ANKRD50 0.77616647 0.00217264 EAF2 0.73385631 0.03465156 PMP22 0.44713619 0.00220309 ACER2 0.81769965 0.03468364 ARF4 0.76387404 0.00223255 KIAA1370 0.8310723 0.03478594 ERO1L 0.75005002 0.00224373 C6orf115 0.7920281 0.03480856 KIAA1033 0.74890236 0.00224373 TMEM161B 0.82837568 0.03482004 UBASH3B 0.73513497 0.00225969 SERPINB4 0.58217203 0.03526646 CARD6 0.74899398 0.00228664 TMEM206 0.76722577 0.03530246 RABGEF1 0.71844668 0.00230748 TMEM87A 0.81927656 0.03544177 MZT1 0.71720898 0.00230944 TAOK3 0.79902307 0.03567122 ASPHD2 0.74295902 0.00238373 KIF5B 0.83603725 0.03581481 2-Mar 0.72623707 0.00241931 ATP6AP2 0.81457493 0.03586138 PPP1R12A 0.72959311 0.00243185 SPRR3 0.55146539 0.03606441 TRA2A 0.7429305 0.00243585 BTBD10 0.80108306 0.03618119 TRAPPC6B 0.73528091 0.00244989 CBR4 0.81257455 0.03620449 RAP2C 0.68175561 0.0024659 LAD1 0.80458232 0.03629508 C6orf62 0.75844544 0.00251409 SMC2 0.82005575 0.03648829 PPIP5K2 0.78387164 0.00252188 MOSPD2 0.61436673 0.03648829 TGFBI 0.52785345 0.00252749 NPAS2 0.83232392 0.03656964 RB1 0.77191438 0.00252877 FBXO32 0.80298304 0.03658334 IMPA1 0.78178293 0.00254095 PLEKHA2 0.80322887 0.03677678 TNPO1 0.78650015 0.00256633 KLHL2 0.79563549 0.03677678 FBXO28 0.77608259 0.00259197 RPH3AL 0.79452691 0.03677678 GALNT7 0.78732986 0.0026183 AGFG1 0.79019227 0.03677678 C1D 0.71982264 0.00262033 MYO6 0.83241148 0.03684746 ACVR2A 0.74257908 0.00262047 AEBP2 0.80355723 0.03686652 FAM18B1 0.76176472 0.00262281 CREB3L2 0.84749284 0.03709572 CXCL6 0.33096087 0.00262687 RANBP9 0.81802251 0.03709572 ERBB2IP 0.7639335 0.00266838 KLHL15 0.65857368 0.03709572 APOBEC3B 0.59242482 0.00270511 CUL3 0.8096363 0.03710186 DHRS9 0.75871115 0.002728 RAB22A 0.80433101 0.03711539 PIGA 0.73677237 0.00273775 OSBPL11 0.78407533 0.0371207 DUSP5 0.6422383 0.00276958 KIAA1539 0.69819167 0.03714167 CLIC4 0.73379796 0.00278346 DLG1 0.83009251 0.03726826 TMEM139 0.75516298 0.00278911 UBXN2B 0.7072684 0.03738914 SMAGP 0.75555643 0.00280753 IRAK4 0.79536496 0.03758668 PDCD4 0.75886671 0.00281775 PI3 0.58243222 0.03758668 PSMC6 0.75273204 0.00282496 C2orf69 0.80329365 0.03766295 MMP13 0.57119817 0.00284506 ZFAND2A 0.77084332 0.03768355 LLPH 0.73355098 0.00288026 APAF1 0.66297493 0.0378646 WBP5 0.71785926 0.0028814 GCOM1 0.68735303 0.03797817 ANKRD36 0.67810421 0.0028814 CA13 0.80329168 0.03802656 ERGIC2 0.76423191 0.00290561 CASP3 0.82104836 0.03806237 KLF3 0.78570378 0.00290614 CPEB2 0.77921871 0.03806237 ZNF770 0.78511401 0.00290848 IPCEF1 0.7139869 0.03808773 ATP11B 0.75855302 0.00291572 CHIC1 0.82883135 0.0381983 SLC16A7 0.7565461 0.00298357 TMTC1 0.78485797 0.03831128 ST3GAL4 0.72572041 0.00300271 USMG5 0.79549212 0.03832104 PPP3CA 0.7448162 0.00304887 FRYL 0.84203988 0.03853779 ZNF117 0.50142805 0.00306525 RASAL1 0.75179941 0.0387072 KDM6A 0.77213154 0.00308418 NBN 0.83154425 0.03872393 PLXND1 0.72142004 0.00308418 HIVEP2 0.78765473 0.03881849 MIER1 0.73557856 0.00313244 TXLNG 0.83712784 0.03882687 OVOL1 0.62502792 0.00317568 DOCK5 0.64601096 0.03890144 SERINC1 0.75179781 0.00321045 LPHN2 0.79892749 0.03891655 RNF13 0.72052005 0.00322686 CRNKL1 0.798853 0.03894719 ZNF323 0.77734232 0.00324034 LYPLAL1 0.79886604 0.03899625 NCOA4 0.74867373 0.00324034 SPPL2A 0.80742034 0.03902383 MTAP 0.75495838 0.00324226 CORO1C 0.7980739 0.03903911 NUFIP2 0.77357636 0.00325406 PANK3 0.83224164 0.03915089 EREG 0.33784392 0.00333776 RMND5A 0.79488445 0.03951253 RAB9A 0.75777512 0.00340898 SKIL 0.76881016 0.03955317 CTSL2 0.55240955 0.00342468 EXOC6 0.81125111 0.03955891 TMEM87B 0.78519368 0.00346666 LOC100294145 0.80974179 0.03965787 NCKAP1 0.78570783 0.00352262 CYLD 0.79867583 0.03971547 ACTG1 0.76392092 0.00353277 C6orf204 0.77428898 0.03971547 STEAP1 0.70400557 0.0035547 MAP3K5 0.80607409 0.03976224 C20orf54 0.6725607 0.00357863 PRKAA2 0.82840521 0.03988755 GTF2A2 0.75863446 0.00358684 CHUK 0.81785294 0.04058768 LAMP2 0.72705142 0.0035881 SNX6 0.81732751 0.04097796 B4GALT4 0.76856871 0.00359353 PSMB2 0.82520067 0.04109294 ETFDH 0.75965073 0.00359783 F3 0.84871606 0.04152053 BLNK 0.75809879 0.00362427 CHST2 0.77943848 0.04178592 FREM2 0.72246394 0.00366469 STX3 0.67806804 0.04184764 PSMD12 0.76433814 0.00368788 MBD2 0.8052338 0.04189529 SRP72 0.7794528 0.00375595 MKLN1 0.82564266 0.04192489 PLEKHF2 0.77591424 0.0038141 LNPEP 0.81160431 0.04207684 TMX1 0.77242467 0.00382017 USP15 0.57814041 0.042141 CD2AP 0.78829185 0.00383168 QKI 0.66036133 0.04236353 SPIRE1 0.74145864 0.0038936 DERL2 0.80411723 0.0425095 MYD88 0.71278412 0.00392321 ZMAT3 0.81595879 0.04264891 SLMAP 0.80047015 0.00393122 ARFGEF1 0.8346722 0.04298754 TUBB6 0.64642059 0.00397194 ERP44 0.80464897 0.04298754 ADAMDEC1 0.56927435 0.00403827 HR 0.7668347 0.04298754 BCL2L15 0.7904988 0.00404876 PITPNC1 0.77723239 0.04308056 DDX21 0.77375237 0.0040688 CCDC59 0.76646023 0.04319013 TOPORS 0.72470814 0.00408953 PHF14 0.83670922 0.0432236 ARMC1 0.78022166 0.0041395 ACP5 0.70586156 0.04325972 DTWD2 0.7787722 0.0041562 ARPC2 0.79251427 0.04329313 FMR1 0.77028713 0.00419389 WDFY3 0.81539874 0.04355816 LIN54 0.74726623 0.00423614 STK17B 0.59142405 0.04356623 KRT23 0.7309985 0.00423614 ATL3 0.81419607 0.04369002 CAV2 0.77823069 0.00428967 FAM84B 0.81682318 0.04373954 KLHL24 0.78910432 0.00432043 SRSF1 0.84262736 0.04402008 EPB41L5 0.74889943 0.00437807 LRRC4 0.76990857 0.04408044 CAV1 0.63489736 0.00443521 EPT1 0.82795078 0.04408619 PNP 0.67837892 0.00444139 CDC42 0.82028228 0.04412194 SRSF3 0.76672922 0.00446884 NBEAL1 0.84458841 0.04417812 PLOD2 0.77561134 0.00450756 CLTC 0.83625892 0.04423619 ATP6V1A 0.76889678 0.00450756 KAT2B 0.80534479 0.04435063 A2ML1 0.612115 0.00451131 NDFIP2 0.83214986 0.0444398 ETF1 0.75295148 0.00452275 PEX11A 0.81101355 0.04453493 PPP2CA 0.76256592 0.00459161 NSF 0.83222465 0.04459514 SLC16A4 0.69724257 0.00459161 MRPS36 0.78965942 0.04459514 TPD52L1 0.75565633 0.00462225 IFNGR2 0.72554575 0.04459514 ABI1 0.78984533 0.00462963 PPM1D 0.75457637 0.0446064 HSPB8 0.54030013 0.00463892 CCDC90B 0.83348758 0.04465495 RAP1A 0.6286857 0.00466577 KRR1 0.8321851 0.04472713 UBE2D3 0.71948245 0.00469068 S100A2 0.55244156 0.04472713 ANKRD36BP1 0.75516672 0.00472447 SPAST 0.82037816 0.04490377 ZMPSTE24 0.78103406 0.0047778 NFYB 0.80065627 0.0449696 EIF4E 0.7660037 0.00485502 RBM27 0.83065796 0.04524741 EIF2S1 0.77037082 0.0048821 FBXO30 0.81207512 0.04524741 TIMP3 0.595252 0.00491633 C16orf87 0.8049152 0.04524741 RPS6KB1 0.77598677 0.0049242 FUT1 0.79442719 0.04556648 NMD3 0.77550502 0.0049698 SNX27 0.81137971 0.04590608 ZNF148 0.76729032 0.00501501 TGFA 0.80946531 0.04594414 GLRX 0.72655698 0.0050292 SNAP23 0.76908603 0.04621429 T0R1AIP2 0.75049332 0.00505042 SS18L2 0.75904606 0.04629091 PDCD10 0.77565396 0.00508211 MED13L 0.80323764 0.04639414 MALT1 0.75049905 0.00508211 KHDRBS3 0.79154107 0.04641655 CHD1 0.66214755 0.00508211 ZNF165 0.76560285 0.04651954 XKRX 0.73215187 0.00508311 RASA2 0.77538631 0.04658899 SPOPL 0.67456908 0.00509812 RGS10 0.78835868 0.04662598 D4S234E 0.74950027 0.0051853 RPP30 0.8120508 0.04690347 ZNF217 0.7862703 0.0052441 LIPA 0.83791908 0.04694484 C3orf14 0.73804789 0.00525477 ZNF438 0.62962389 0.04694484 ZFX 0.78085119 0.00529941 LIMCH1 0.83370853 0.04700596 FAM59A 0.7610016 0.0053185 LMO7 0.82293913 0.04710612 LAMTOR3 0.75345856 0.00532764 PUS7L 0.80031465 0.04718282 HK2 0.78199641 0.00534013 CBFB 0.82243007 0.04719184 GOLT1B 0.78276656 0.0053411 LMBRD1 0.81532931 0.04726984 TF 0.53399053 0.00534914 RIPK2 0.69796908 0.04754754 SLC12A2 0.76713817 0.00541558 SLC36A4 0.77616278 0.04774991 BLZF1 0.76183931 0.00543208 NR4A3 0.31905163 0.04778283 MORC3 0.77320595 0.0054433 TTC13 0.79548927 0.04780477 ABHD13 0.75751055 0.0054433 PRRC1 0.84094443 0.0480836 ARHGAP10 0.76095515 0.0055016 TOMM70A 0.83565352 0.0480836 PPP6C 0.78390582 0.00565944 EIF4A3 0.79211732 0.04817496 AKTIP 0.76242019 0.00566109 FRG1 0.7766039 0.04833913 IL18 0.74117905 0.00571372 DIP2B 0.81299057 0.048344 AMMECR1 0.7666803 0.00572446 MRPL50 0.83249841 0.04843281 SMEK1 0.78090529 0.0057997 SHISA9 0.76315554 0.04871027 NXT2 0.76719049 0.00584548 ITGAX 0.21887106 0.0489067 C12orf5 0.74487036 0.00585798 FAM120AOS 0.80855619 0.04915381 NFE2L3 0.77997497 0.00588459 MAP3K1 0.81117229 0.04919247 SFIOC2 0.76830128 0.00591428 BRMS1L 0.78256727 0.04924817 ERI1 0.72854148 0.00591448 ST3GAL5 0.81440085 0.04925387 ZDHHC20 0.78918118 0.00595532 RALBP1 0.82325491 0.04929206 MS4A7 0.50459021 0.00595907 GTPBP10 0.83111393 0.04933293 CTR9 0.77182568 0.00597991 DOCK4 0.8068281 0.04934341 FAM46A 0.78379873 0.005986 WDR26 0.8064914 0.04935751 CPA4 0.73474526 0.005986 CTH 0.74246418 0.04943839 TROVE2 0.71896413 0.00601438 PARP9 0.8069565 0.04958092 ARL6IP1 0.78399879 0.00601695 ANKHD1 0.68180395 0.04988035 GADD45A 0.7103299 0.00619164 TRNT1 0.82420431 0.04988205 YOD1 0.60396183 0.00619164 C15orf48 0.66963309 0.04988205 CTTNBP2NL 0.76796852 0.00625618 FERMT2 0.80386104 0.04991843 PLSCR4 0.79632728 0.00626049 REACTOME_IMMUNE_SYSTEM Genes involved 1.07E−22 in Immune System TMEM188 0.72279412 0.00632262 REACTOME_METABOLISM_OF_LIP- Genes involved 1.47E−18 IDS_AND_LIPOPROTEINS in Metabolism of lipids and lipoproteins MMADHC 0.78690813 0.00643294 REACTOME_ADAPTIVE_IMMUNE_SYSTEM Genes involved 1.46E−15 in Adaptive Immune System ARG2 0.74715273 0.00650999 REACTOME_HEMOSTASIS Genes involved 1.57E−14 in Hemostasis SLC30A6 0.7797098 0.00651052 PID_ERBB1_DOWNSTREAM_PATHWAY ErbB1 2.05E−13 downstream signaling SPRR2A 0.37077622 0.0065136 REACTOME_PPARA_ACTIVATES_GENE_EXPRESSION Genes involved 1.47E−12 in PPARA Activates Gene Expression SPINK5 0.54459219 0.00663235 PID_PDGFRB_PATHWAY PDGFR-beta 2.22E−12 signaling pathway YWHAG 0.78943324 0.00664564 PID_P53_DOWNSTREAM_PATHWAY Direct p53 8.30E−12 effectors IFI16 0.78293982 0.00669397 KEGG_PATHWAYS_IN_CANCER Pathways in 1.14E−11 cancer CYP4F3 0.66425151 0.00672128 REACTOME_FATTY_ACID_TRIACYL- Genes involved 1.65E−11 GLYCEROL_AND_KETONE_BODY_METABOLISM in Fatty acid, triacylglycerol, and ketone body metabolism DSG2 0.79997277 0.00672627 NABA_MATRISOME_ASSOCIATED Ensemble of 2.28E−10 genes encoding ECM-associated proteins including ECM-affilaited proteins, ECM regulators and secreted factors ITGB1 0.78721307 0.00683767 REACTOME_TRANSMEMBRANE_TRANS- Genes involved 2.48E−09 PORT_OF_SMALL_MOLECULES in Transmembrane transport of small molecules SGMS2 0.80465915 0.00686207 REACTOME_INNATE_IMMUNE_SYSTEM Genes involved 4.47E−09 in Innate Immune System DMXL2 0.75565891 0.00687227 KEGG_REGULATION_OF_ACTIN_CYTOSKELETON Regulation of 5.03E−09 actin cytoskeleton UGP2 0.77377034 0.00689688 KEGG_MAPK_SIGNALING_PATHWAY MAPK signaling 6.01E−09 pathway TMEM165 0.76973779 0.00694615 REACTOME_DIABETES_PATHWAYS Genes involved 7.31E−09 in Diabetes pathways CDC73 0.76294135 0.00696238 KEGG_SMALL_CELL_LUNG_CANCER Small cell lung 7.31E−09 cancer MPP5 0.80257658 0.00703803 NABA_ECM_REGULATORS Genes encoding 7.31E−09 enzymes and their regulators involved in the remodeling of the extracellular matrix SP1 0.76405586 0.00705511 REACTOME_APOPTOSIS Genes involved 7.61E−09 in Apoptosis VDAC2 0.76968598 0.00707017 NABA_MATRISOME Ensemble of 1.09E−08 genes encoding extracellular matrix and extracellular matrix-associated proteins LRRFIP1 0.77118612 0.0070728 PID_NFKAPPAB_CANONICAL_PATHWAY Canonical NF- 1.11E−08 kappaB pathway C14orfl28 0.71927857 0.00711871 KEGG_APOPTOSIS Apoptosis 1.29E−08 LYPD3 0.68004615 0.00715007 REACTOME_CLASS_I_MHC_MEDIATED_ANTI- Genes involved 1.98E−08 GEN_PROCESSING_PRESENTATION in Class I MHC mediated antigen processing & presentation PTPRZ1 0.78817053 0.00719019 REACTOME_TOLL_RECEPTOR_CASCADES Genes involved 2.71E−08 in Toll Receptor Cascades RAB18 0.76366275 0.00722127 REACTOME_ACTIVATED_TLR4_SIGNALLING Genes involved 2.71E−08 in Activated TLR4 signalling AP3S1 0.75774232 0.00729569 PID_CDC42_PATHWAY CDC42 signaling 2.71E−08 events C17orf91 0.74332375 0.00730188 KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY NOD-like 4.69E−08 receptor signaling pathway XIAP 0.79828911 0.0073532 KEGG_FOCAL_ADHESION Focal adhesion 7.43E−08 L0C374443 0.71361722 0.00737354 REACTOME_TRAF6_MEDIATED_INDUC- Genes involved 9.93E−08 TION_OF_NFKB_AND_MAP_KINASES_UP- in TRAF6 ON_TLR7_8_OR_9_ACTIVATION mediated induction of NFkB and MAP kinases upon TLR7/8 or 9 activation TWF1 0.79895735 0.00742683 PID_TNF_PATHWAY TNF receptor 1.12E−07 signaling pathway ELF1 0.77273855 0.00744917 KEGG_EPITHELIAL_CELL_SIGNALING_IN_HELICO- Epithelial cell 1.49E−07 BACTER_PYLORI_INFECTION signaling in Helicobacter pylori infection S100A14 0.76635669 0.00744917 BIOCARTA_HIVNEF_PATHWAY HIV-I Nef: 1.71E−07 negative effector of Fas and TNF SLC16A6 0.70750259 0.00745345 KEGG_P53_SIGNALING_PATHWAY p53 signaling 1.71E−07 pathway DCUN1D3 0.56968422 0.00747439 REACTOME_ANTIGEN_PROCESSING_UBIQUI- Genes involved 1.79E−07 TINATION_PROTEASOME_DEGRADATION in Antigen processing: Ubiquitination & Proteasome degradation SLC44A2 0.76320925 0.00753544 PID_AP1_PATHWAY AP-1 1.93E−07 transcription factor network SESTD1 0.7924907 0.00756289 KEGG_PATHOGENIC_ESCHERICHIA_COLI_INFECTION Pathogenic 1.93E−07 Escherichia coli infection S100P 0.64809558 0.00767001 REACTOME_MYD88_MAL_CASCADE_INITI- Genes involved 2.31E−07 ATED_ON_PLASMA_MEMBRANE in MyD88: Mal cascade initiated on plasma membrane ARPP19 0.78635202 0.00768701 REACTOME_SIGNALLING_BY_NGF Genes involved 2.51E−07 in Signalling by NGF KLF10 0.76312973 0.00775452 KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS Ubiquitin 2.51E−07 mediated proteolysis TGM1 0.55760183 0.00777418 REACTOME_CYTOKINE_SIGNAL- Genes involved 2.56E−07 ING_IN_IMMUNE_SYSTEM in Cytokine Signaling in Immune system BHLHE40 0.78959699 0.00777685 KEGG_NEUROTROPHIN_SIGNALING_PATHWAY Neurotrophin 3.27E−07 signaling pathway PLBD1 0.70356721 0.00777685 REACTOME_TRIF_MEDIATED_TLR3_SIGNALING Genes involved 3.49E−07 in TRIF mediated TLR3 signaling MYC 0.76472327 0.00781167 BIOCARTA_MAPK_PATHWAY MAPKinase 3.88E−07 Signaling Pathway FAM91A1 0.77751938 0.00785683 REACTOME_MEMBRANE_TRAFFICKING Genes involved 4.44E−07 in Membrane Trafficking MREG 0.76267651 0.00794736 BIOCARTA_SALMONELLA_PATHWAY How does 4.71E−07 salmonella hijack a cell GDPD1 0.81908069 0.0079732 PID_HIF1_TFPATHWAY HIF-1-alpha 6.39E−07 transcription factor network GPD2 0.80071021 0.00805078 PID_TGFBR_PATHWAY TGF-beta 6.45E−07 receptor signaling PVRL4 0.77402462 0.00805078 PID_MYC_ACTIV_PATHWAY Validated targets 7.35E−07 ofC-MYC transcriptional activation SUCLA2 0.76523468 0.00805078 BIOCARTA_ACTINY_PATHWAY Y branching of 7.40E−07 actin filaments ACER3 0.77959865 0.00808456 REACTOME_PHOSPHOLIPID_METABOLISM Genes involved 7.42E−07 in Phospholipid metabolism RABL3 0.7748714 0.00809777 PID_MET_PATHWAY Signaling events 8.18E−07 mediated by Hepatocyte Growth Factor Receptor (c-Met) RAB10 0.79901305 0.0082063 KEGG_ENDOCYTOSIS Endocytosis 8.35E−07 PJA2 0.7769656 0.00823489 REACTOME_INSULIN_SYNTHESIS_AND_PROCESSING Genes involved 1.08E−06 in Insulin Synthesis and Processing CAP1 0.72655632 0.00826187 KEGG_PANCREATIC_CANCER Pancreatic cancer 1.12E−06 RDX 0.80715808 0.00827579 KEGG_RENAL_CELLCARCINOMA Renal cell 1.12E−06 carcinoma TES 0.79507705 0.00829307 PID_ATF2_PATHWAY ATF-2 1.25E−06 transcription factor network MUDENG 0.79933934 0.0083017 REACTOME_SLC_MEDIATED_TRANS- Genes involved 1.30E−06 MEMBRANE_TRANSPORT in SLC-mediated transmembrane transport PPIL3 0.76235604 0.00834263 REACTOME_SIGNAL- Genes involved 1.40E−06 ING_BY_THE_B_CELL_RECEPTOR_BCR in Signaling by the B Cell Receptor (BCR) BIRC2 0.78625068 0.00837842 PID_FOXO_PATHWAY FoxO family 1.45E−06 signaling CCNB1 0.7807843 0.00847331 REACTOME_NFKB_AND_MAP_KINASES_ACTI- Genes involved 1.46E−06 VATION_MEDIATED_BY_TLR4_SIGNAL- in NFkB and ING_REPERTOIRE MAP kinases activation mediated by TLR4 signaling repertoire ATL2 0.77916813 0.0084764 REACTOME_PLATELET_ACTIVATION_SIGNALING Genes involved 1.48E−06 AND_AGGREGATION in Platelet activation, signaling and aggregation SORD 0.75801895 0.0084879 KEGG_TGF_BETA_SIGNALING_PATHWAY TGF-beta 1.74E−06 signaling pathway ATP11C 0.79291526 0.00853151 PID_EPHB_FWD_PATHWAY EPHB forward 1.77E−06 signaling RRAGC 0.75615041 0.00853151 REACTOME_APOPTOTIC_CLEA- Genes involved 1.77E−06 VAGE_OF_CELLULAR_PROTEINS in Apoptotic cleavage of cellular proteins IFNGR1 0.69711126 0.00853151 BIOCARTA_CDC42RAC_PATHWAY Role of PI3K 2.02E−06 subunit p85 in regulation of Actin Organization and Cell Migration STEAP2 0.78974481 0.00856925 REACTOME_CELL_CYCLE_MITOTIC Genes involved 2.04E−06 in Cell Cycle, Mitotic WDR72 0.64839931 0.0086094 PID_CASPASE_PATHWAY Caspase cascade 2.45E−06 in apoptosis KRT4 0.67492283 0.00863552 REACTOME_CIRCADIAN_CLOCK Genes involved 2.97E−06 in Circadian Clock HS2ST1 0.7871526 0.00868303 ST_FAS_SIGNALING_PATHWAY Fas Signaling 3.14E−06 Pathway ZCCHC10 0.75926787 0.00868842 BIOCARTA_DEATH_PATHWAY Induction of 3.18E−06 apoptosis through DR3 and DR4/5 Death Receptors PPP2R2A 0.79190305 0.00877521 PID_RAC1_PATHWAY RAC1 signaling 3.49E−06 pathway SQRDL 0.75607401 0.00879068 SIG_PIP3_SIGNALING_IN_CARDIAC_MYOCTES Genes related to 4.27E−06 PIP3 signaling in cardiac myocytes STK38 0.78754071 0.00886943 PID_BETA_CATENIN_NUC_PATHWAY Regulation of 4.37E−06 nuclear beta catenin signaling and target gene transcription LYRM1 0.7382844 0.00898135 REACTOME_APOPTOTIC_CLEA- Genes involved 5.72E−06 VAGE_OF_CELL_ADHESION_PROTEINS in Apoptotic cleavage of cell adhesion proteins SYK 0.64957988 0.00898135 PID-PLK1_PATHWAY PLK1 signaling 6.25E−06 events S100A10 0.76365242 0.00900115 REACTOME_METABOLISM_OF_PROTEINS Genes involved 6.47E−06 in Metabolism of proteins NTS 0.73291849 0.00900309 REACTOME_BMAL1_CLOCK_NPAS2_ACTI- Genes involved 6.56E−06 VATES_CIRCADIAN_EXPRESSION in BMAL1: CLOCK/ NPAS2 Activates Circadian Expression LOC440434 0.68882777 0.00901276 ST_P38_MAPK_PATHWAY p38 MAPK 8.35E−06 Pathway GNA13 0.63583346 0.00908917 REACTOME_DEVELOPMENTAL_BIOLOGY Genes involved 9.75E−06 in Developmental Biology STK17A 0.73661542 0.00912019 PID_ARF6_TRAFFICKING_PATHWAY Arf6 trafficking 1.10E−05 events ITSN2 0.76584981 0.00913286 ST_TUMOR_NECROSIS_FACTOR_PATHWAY Tumor Necrosis 1.23E−05 Factor Pathway. GOLT1A 0.71280825 0.00924664 PID_ECADHERIN_NASCENT_AJ_PATHWAY E-cadherin 1.29E−05 signaling in the nascent adherens junction DIAPH1 0.77552848 0.00932056 REACTOME_MAP_KINASE_ACTI- Genes involved 1.29E−05 VATION_IN_TLR_CASCADE in MAP kinase activation in TLR cascade ZNF654 0.74649612 0.00934308 KEGG_B_CELL_RECEPTOR_SIGNALING_PATHWAY B cell receptor 1.31E−05 signaling pathway FPR3 0.48825296 0.00934423 BIOCARTA_MITOCHONDRIA_PATHWAY Role of 1.40E−05 Mitochondria in Apoptotic Signaling RCHY1 0.79749711 0.00935 REACTOME_SIGNAL- Genes involved 1.48E−05 ING_BY_TGF_BETA_RECEPTOR_COMPLEX in Signaling by TGF-beta Receptor Complex 4-Mar 0.77086317 0.00935 SIG_INSULIN_RECEPTOR_PATH- Genes related to 1.49E−05 WAY_IN_CARDIAC_MYOCYTES the insulin receptor pathway REEP3 0.8126155 0.0094555 REACTOME_NOD1_2_SIGNALING_PATHWAY Genes involved 1.49E−05 in NOD1/2 Signaling Pathway TFG 0.79338065 0.00956122 ST_JNK_MAPK_PATHWAY JNK MAPK 1.49E−05 Pathway SNX18 0.76111449 0.00960834 REACTOME_MITOTIC_G1_G1_S_PHASES Genes involved 1.59E−05 in Mitotic G1- G1/S phases TMEM79 0.77640651 0.00962273 REACTOME_NGF_SIGNAL- Genes involved 1.59E−05 LING_VIA_TRKA_FROM_THE_PLASMA_MEMBRANE in NGF signalling via TRKA from the plasma membrane C12orf35 0.56826344 0.00962273 REACTOME_ACTIVA- Genes involved 1.63E−05 TION_OF_NF_KAPPAB_IN_B_CELLS in Activation of NF-kappaB in B Cells GOLGA4 0.8023233 0.00962569 PID_AVB3_OPN_PATHWAY Osteopontin- 1.85E−05 mediated events PLA2R1 0.78448235 0.00972618 PID_CD40_PATHWAY CD40/CD40L 1.85E−05 signaling SYPL1 0.80241463 0.00979309 PID_RB_1PATHWAY Regulation of 1.86E−05 retinoblastoma protein C15orf34 0.76100423 0.0098085 PID_TAP63_PATHWAY Validated 2.31E−05 transcriptional targets of TAp63 isoforms AGA 0.77317636 0.00987069 REACTOME_APOPTOTIC_EXECUTION_PHASE Genes involved 2.31E−05 in Apoptotic execution phase 10-Sep 0.80194663 0.00988696 ST_ERK1_ERK2_MAPK_PATHWAY ERK1/ERK2 2.31E−05 MAPK Pathway MFAP3 0.78771375 0.00994587 BIOCARTA_CASPASE_PATHWAY Caspase Cascade 2.41E−05 in Apoptosis PID_INTEGRIN3_PATHWAY Beta3 integrin 2.55E−05 cell surface interactions

TABLE 3 List of known asthma-associated genes³⁷ that overlap with genes in the RNAseq data sets. Number of Genes Genes 70 ACE; ACO1; ACP1; ADRB2; ALOX5; C11orf71; C3; C3AR1; C5orf56; CCL5; CCR5; CD14; CDK2; CFTR; CHML; CRCT1; CYFIP2; DAP3; DEFB1; DENND1B; GAB1; GATA3; GSDMB; GSTP1; GSTT1; HAVCR2; HLA-DOA; HLA-DPA1; HLA-DPB1; HLA-DQA1; HLA-DQB1; HLA-DRA; HLA-DRB1; HNMT; IKZF4; IL15; IL18; IL1B; IL1R1; IL1RN; IL2RB; IL33; IL5RA; IL6R; IL8; IRAK2; IRF1; NDFIP1; NOD1; OPN3; ORMDL3; PBX2; PCDH20; PDE4D; PHF11; RAD50; RORA; SERPINA3; SLC22A5; SMAD3; SPATS2L; SPINK5; STAT6; TAP1; TGFB1; TIMP1; TLE4; TLR2; TLR4; VDR

TABLE 4 List of the genes identified in the eight classification models and unique genes comprising the asthma gene panel. Model/Asthma Number Optimal Classification Panel subset of Genes Genes Threshold LR-RFE & 90 PCSK6, HIPK2, TXNDC5, B3GNT6, CD177, Approx 0.76 Logistic KRT24, FCGBP, DLEC1, SERPINB3, CLEC2B, PTER, ERAP2, SYNM, CDKN1A, SPRR1A, C12orf36, SERPINE2, XIST, SLC9A3, SCD, TEKT2, EPPK1, RPH3AL, MS4A8B, SDK1, IGF1, FOS, SERPINB11, CPA3, HLA.C, SLC26A4, CYP1B1, SCGB1A1, SEMA5A, ESR1, CDHR3, NWD1, TMEM190, GNAL, ZNF117, EPDR1, DEFB1, PTAFR, SPRR2D, CHCHD10, LOC90784, AKR1B15, CROCCP2, S100A8, TFPI, C3, S100A7, DUSP1, LY6D, SORD, SERPINF1, TPSB2, NMU, GSTT1, LPAR6, CYFIP2, CPAMD8, SLC5A8, SLC5A3, SC4MOL, NR1D1, ARL4D, ALDH1A3, LPHN1, LOC286002, CRABP2, CEBPD, C6orf105, TM4SF1, ANKRD9, PCP4L1, SLC35E2, LOC388564, DNAI1, SLC44A5, LTBP1, CROCC, NCRNA00152, CDH26, TPSAB1, RHCG, CLEC7A, IER3, MMP9, ALOX15B LR-RFE & 90 PCSK6, HIPK2, TXNDC5, B3GNT6, CD177, Approx 0.52 SVM-Linear KRT24, FCGBP, DLEC1, SERPINB3, CLEC2B, PTER, ERAP2, SYNM, CDKN1A, SPRR1A, C12orf36, SERPINE2, XIST, SLC9A3, SCD, TEKT2, EPPK1, RPH3AL, MS4A8B, SDK1, IGF1, FOS, SERPINB11, CPA3, HLA.C, SLC26A4, CYP1B1, SCGB1A1, SEMA5A, ESR1, CDHR3, NWD1, TMEM190, GNAL, ZNF117, EPDR1, DEFB1, PTAFR, SPRR2D, CHCHD10, LOC90784, AKR1B15, CROCCP2, S100A8, TFPI, C3, S100A7, DUSP1, LY6D, SORD, SERPINF1, TPSB2, NMU, GSTT1, LPAR6, CYFIP2, CPAMD8, SLC5A8, SLC5A3, SC4MOL, NR1D1, ARL4D, ALDH1A3, LPHN1, LOC286002, CRABP2, CEBPD, C6orf105, TM4SF1, ANKRD9, PCP4L1, SLC35E2, LOC388564, DNAI1, SLC44A5, LTBP1, CROCC, NCRNA00152, CDH26, TPSAB1, RHCG, CLEC7A, IER3, MMP9, ALOX15B SVM-RFE & 119 PYCR1, TXNDC5, B3GNT6, CD177, FAM46C, Approx 0.64 SVM-Linear PPP2R2C, VWA1, PTER, KAL1, GNG4, ERAP2, SYNM, CCL5, TRIM31, DOCK1, NFKBIZ, MGST1, SPRR1A, PLIN4, TNFRSF18, ISYNA1, SLC9A4, SLC9A2, SLC9A3, CPA3, SERPINB11, OSM, MSMB, LGALS9C, SDK1, G0S2, DPYSL3, RPH3AL, KIF7, C11orf9, COL1A1, HLA.C, HCAR2, SLC26A4, SHF, SERPINF1, SPRR2D, SCGB1A1, ZDHHC2, SEMA5A, ESR1, VAV2, NWD1, CYP2E1, KRT13, KRT10, GNAL, ZNF117, EPDR1, PAX3, KLHL29, NBPF1, GPNMB, FABP5, CLCA2, C7orf13, SPRR2F, LOC90784, CYP2B6, CROCCP2, TFPI, S100A7, DUSP1, LY6D, PHYHD1, SORD, TMEM64, C15orf48, MXRA8, IL4I1, TPSB2, NMU, BPIFA2, ZNF528, HTR3A, STEAP1, STEAP2, LPAR6, OBSCN, MT2A, CPAMD8, D4S234E, ECM1, SLC16A4, LRRC26, CRCT1, SLC5A5, ZC3H12A, NR1D1, ALDH1A3, SLC37A2, LPHN1, CRABP2, TM4SF1, ANKRD9, CXCR7, TF, TMEM220, LOC388564, XIST, SLC44A5, LTBP1, RAB3B, MEX3D, TPSAB1, RHCG, SRRM3, SCGB3A1, RND1, REC8, SCD, ALOX15B, ATP6V0E2, COL6A6 SVM-RFE & 119 PYCR1, TXNDC5, B3GNT6, CD177, FAM46C, Approx 0.69 Logistic PPP2R2C, VWA1, PTER, KAL1, GNG4, ERAP2, SYNM, CCL5, TRIM31, DOCK1, NFKBIZ, MGST1, SPRR1A, PLIN4, TNFRSF18, ISYNA1, SLC9A4, SLC9A2, SLC9A3, CPA3, SERPINB11, OSM, MSMB, LGALS9C, SDK1, G0S2, DPYSL3, RPH3AL, KIF7, C11orf9, COL1A1, HLA.C, HCAR2, SLC26A4, SHF, SERPINF1, SPRR2D, SCGB1A1, ZDHHC2, SEMA5A, ESR1, VAV2, NWD1, CYP2E1, KRT13, KRT10, GNAL, ZNF117, EPDR1, PAX3, KLHL29, NBPF1, GPNMB, FABP5, CLCA2, C7orf13, SPRR2F, LOC90784, CYP2B6, CROCCP2, TFPI, S100A7, DUSP1, LY6D, PHYHD1, SORD, TMEM64, C15orf48, MXRA8, IL4I1, TPSB2, NMU, BPIFA2, ZNF528, HTR3A, STEAP1, STEAP2, LPAR6, OBSCN, MT2A, CPAMD8, D4S234E, ECM1, SLC16A4, LRRC26, CRCT1, SLC5A5, ZC3H12A, NR1D1, ALDH1A3, SLC37A2, LPHN1, CRABP2, TM4SF1, ANKRD9, CXCR7, TF, TMEM220, LOC388564, XIST, SLC44A5, LTBP1, RAB3B, MEX3D, TPSAB1, RHCG, SRRM3, SCGB3A1, RND1, REC8, SCD, ALOX15B, ATP6V0E2, COL6A6 LR-RFE & 90 PCSK6, HIPK2, TXNDC5, B3GNT6, CD177, Approx 0.49 AdaBoost KRT24, FCGBP, DLEC1, SERPINB3, CLEC2B, PTER, ERAP2, SYNM, CDKN1A, SPRR1A, C12orf36, SERPINE2, XIST, SLC9A3, SCD, TEKT2, EPPK1, RPH3AL, MS4A8B, SDK1, IGF1, FOS, SERPINB11, CPA3, HLA.C, SLC26A4, CYP1B1, SCGB1A1, SEMA5A, ESR1, CDHR3, NWD1, TMEM190, GNAL, ZNF117, EPDR1, DEFB1, PTAFR, SPRR2D, CHCHD10, LOC90784, AKR1B15, CROCCP2, S100A8, TFPI, C3, S100A7, DUSP1, LY6D, SORD, SERPINF1, TPSB2, NMU, GSTT1, LPAR6, CYFIP2, CPAMD8, SLC5A8, SLC5A3, SC4MOL, NR1D1, ARL4D, ALDH1A3, LPHN1, LOC286002, CRABP2, CEBPD, C6orf105, TM4SF1, ANKRD9, PCP4L1, SLC35E2, LOC388564, DNAI1, SLC44A5, LTBP1, CROCC, NCRNA00152, CDH26, TPSAB1, RHCG, CLEC7A, IER3, MMP9, ALOX15B LR-RFE & 90 PCSK6, HIPK2, TXNDC5, B3GNT6, CD177, Approx 0.60 RandomForest KRT24, FCGBP, DLEC1, SERPINB3, CLEC2B, PTER, ERAP2, SYNM, CDKN1A, SPRR1A, C12orf36, SERPINE2, XIST, SLC9A3, SCD, TEKT2, EPPK1, RPH3AL, MS4A8B, SDK1, IGF1, FOS, SERPINB11, CPA3, HLA.C, SLC26A4, CYP1B1, SCGB1A1, SEMA5A, ESR1, CDHR3, NWD1, TMEM190, GNAL, ZNF117, EPDR1, DEFB1, PTAFR, SPRR2D, CHCHD10, LOC90784, AKR1B15, CROCCP2, S100A8, TFPI, C3, S100A7, DUSP1, LY6D, SORD, SERPINF1, TPSB2, NMU, GSTT1, LPAR6, CYFIP2, CPAMD8, SLC5A8, SLC5A3, SC4MOL, NR1D1, ARL4D, ALDH1A3, LPHN1, LOC286002, CRABP2, CEBPD, C6orf105, TM4SF1, ANKRD9, PCP4L1, SLC35E2, LOC388564, DNAI1, SLC44A5, LTBP1, CROCC, NCRNA00152, CDH26, TPSAB1, RHCG, CLEC7A, IER3, MMP9, ALOX15B SVM-RFE & 123 HSPA6, GSTA1, PLIN4, TXNDC5, B3GNT6, Approx 0.50 RandomForest BHLHE40, CYP4F11, CD177, IRX5, TMX4, DDIT4, SCCPDH, FCGBP, ARRDC4, MUC16, TSPAN8, ACOT2, SPINK5, C19orf51, PTER, F2R, GNG4, SERPING1, C14orf167, ERAP2, MMP10, DOCK1, NFKBIZ, CHCHD10, MGST1, C12orf36, CLCA2, XIST, SLC9A2, SLC9A3, CPA3, TEKT2, EPPK1, SERPINB11, OVCA2, MSMB, CDC25B, TNS3, SDK1, FOS, RPH3AL, KIF7, COL1A1, HLA.C, HCAR2, SLC26A4, PAX3, SERPINF1, SPRR2F, DNER, GSTT1, ESR1, VAV2, CYP2E1, TMEM190, KRT13, GNAL, RPSAP58, FABP5, MALAT1, C7orf13, SCGB1A1, AKR1B15, CYP2B6, HBEGF, TFPI, C3, S100A7, DUSP1, HERC2P2, SORD, C15orf48, MXRA8, IL4I1, TPSB2, NMU, SEMA5A, BPIFA2, PRSS3, AK4, BASP1, HTR3A, COL21A1, LPAR6, MKI67, CYFIP2, CPAMD8, D4S234E, CRCT1, MFSD6L, CIT, SLC5A8, NR1D1, ALDH1A3, SLC37A2, LPHN1, LOC286002, CRABP2, CEBPD, ANKRD9, CXCR7, SLC35E2, LOC388564, SLC9A4, SLC44A5, LTBP1, CRYM, RAB3B, KAL1, MEX3D, TPSAB1, NCRNA00086, HLA.DQA1, RHCG, REC8, ALOX15B, ATP6V0E2, COL6A6 SVM-RFE & 212 IDAS, NR1D1, HIPK2, RCBTB2, PYCR1, Approx 0.55 AdaBoost TSPAN8, CPPED1, B3GNT6, HLA.DPB1, PARD6G, IP6K3, EIF1AX, CD177, FAM46C, IRX5, C3orf14, IFITM1, NGEF, SCCPDH, PPP2R2C, XYLT1, DLEC1, MUC16, SERPINB3, ACOT2, SLC35E2, SMPDL3B, C19orf51, LOC388796, MPV17L, SYK, SLC9A4, PTER, F2R, GNG4, BST1, C14orf167, CCNO, ERAP2, SYNM, EVL, CCL5, TRIM31, DOCK1, RRAS, MALAT1, MGST1, SLC29A1, C12orf36, PLIN4, SERPINE2, JUB, PTN, SLC9A2, CLEC7A, CPA3, TEKT2, EPPK1, SERPINB11, OVCA2, OSM, VWA1, CDC25B, LGALS9C, MS4A8B, SDK1, S100A13, DPYSL3, PDLIM2, RPH3AL, KIF7, C11orf9, TEKT4P2, PMEPA1, HLA.C, HCAR2, SLC26A4, PAX3, NLRP1, GIMAP6, SPRR2F, SPRR2C, DNER, ABCG1, ZDHHC2, ZNF532, SEMA5A, ESR1, VAV2, NWD1, CYP2E1, TMEM190, MAOB, CXCR7, GNAL, ZNF117, GAS7, EPDR1, NCF2, DEFB1, H2AFY2, GRTP1, NBPF1, CROCCP2, SERPING1, KRT5, CHCHD10, TP63, C7orf13, SCGB1A1, LOC90784, HIC1, AKR1B15, GAS2L2, HIFX, CYP2B6, GPNMB, HBEGF, ACAT2, TFPI, C3, S100A7, DUSP1, SLC9A3, LYSMD2, HERC2P2, PHYHD1, TOP1MT, PLCL2, SORD, TMEM64, C15orf48, PLXND1, CD8A, MXRA8, IL4I1, IL2RB, NMU, GSTT1, BPIFA2, ZNF528, IL32, WDR96, NPNT, DMRTA2, BASP1, CEBPD, HTR3A, COL21A1, OBSCN, CYFIP2, CPAMD8, XIST, D4S234E, IGF1R, ECM1, PTPRZ1, CRCT1, RRM2, MLKL, CIT, SC4MOL, DDIT4, ELF5, ARL4D, ALDH1A3, SLC37A2, LPHN1, LOC286002, CRABP2, CCNJL, MEGF6, TM4SF1, ANKRD9, C8orf4, SLC16A14, ALOX15B, PCP4L1, TOR1B, TF, ACOT11, HOMER3, LOC388564, CYP1B1, DNAI1, LRP12, LTBP1, ANXA6, CARD11, CROCC, CES1, ALDH3B2, NCRNA00152, RAB3B, TNC, KAL1, FOXN4, MEX3D, FCGBP, TPSAB1, NCRNA00086, HLA.DOA, KRT78, RHCG, NCALD, REC8, RDH10, SERPINF1, ATP6V0E2, POLR2J3, POU2F3, TCTEX1D4 Asthma gene 275 IDAS, HSPA6, PCSK6, HIPK2, C15orf48, n/a panel (275 TXNDC5, CPPED1, HLA.DPB1, PARD6G, unique genes) CYP4F11, FAM46C, IRX5, C3orf14, IGF1R, NGEF, SCCPDH, PPP2R2C, MUC16, ACOT2, SMPDL3B, C19orf51, MPV17L, SYK, CLEC2B, PTER, F2R, BST1, SYNM, EVL, CDKN1A, DOCK1, G0S2, MGST1, C12orf36, PLIN4, SERPINE2, JUB, SLC9A2, CLEC7A, TEKT2, EPPK1, OVCA2, MSMB, LGALS9C, MS4A8B, SDK1, PDLIM2, FOS, RPH3AL, KIF7, COL1A1, TEKT4P2, HLA.C, PAX3, SPRR2D, GIMAP6, SPRR2F, SPRR2C, DNER, ZDHHC2, GSTT1, ESR1, CDHR3, CYP2E1, TMEM190, BHLHE40, KRT13, KRT10, GNAL, RPSAP58, EPDR1, H2AFY2, GRTP1, NBPF1, SERPING1, PTAFR, KRT5, CHCHD10, HIC1, ZNF532, CROCCP2, HBEGF, ACAT2, S100A8, TFPI, C3, S100A7, HERC2P2, PLCL2, SORD, CD8A, MXRA8, IL2RB, NMU, LRRC26, BPIFA2, PRSS3, AK4, NPNT, SLC5A3, FCGBP, HTR3A, COL21A1, SLC5A5, MT2A, CYFIP2, XIST, ECM1, PTPRZ1, SLC5A8, MFSD6L, MLKL, ZC3H12A, ALDH1A3, SLC37A2, LOC286002, CCNJL, MEGF6, TM4SF1, SLC16A14, CXCR7, HOMER3, CYP1B1, ALDH3B2, SLC44A5, LTBP1, ANXA6, IL32, CDH26, MEX3D, VWA1, TPSAB1, HLA.DOA, ARRDC4, DMRTA2, SRRM3, IER3, RND1, REC8, RDH10, ATP6V0E2, POLR2J3, COL6A6, PCP4L1, GSTA1, RCBTB2, PYCR1, TSPAN8, B3GNT6, EIF1AX, CD177, PLXND1, IFITM1, DDIT4, KLHL29, KRT24, XYLT1, DLEC1, SERPINB3, IP6K3, TMEM220, LOC388796, KAL1, GNG4, C14orf167, CCNO, ERAP2, CCL5, TRIM31, RRAS, CLCA2, SLC29A1, SPRR1A, ARL4D, PTN, CPA3, OSM, TNS3, S100A13, IGF1, DPYSL3, SERPINB11, CDC25B, C11orf9, PMEPA1, HCAR2, SLC26A4, SHF, LOC90784, SCGB1A1, DNAI1, ABCG1, TMEM64, SEMA5A, CRYM, VAV2, NWD1, MAOB, ZNF117, GAS7, SPINK5, NCF2, DEFB1, KRT78, GPNMB, FABP5, MALAT1, MMP10, TP63, C7orf13, NLRP1, AKR1B15, GAS2L2, H1FX, CYP2B6, IL4I1, DUSP1, LYSMD2, PHYHD1, TOP1MT, SERPINF1, NFKBIZ, TPSB2, ZNF528, WDR96, BASP1, STEAP1, STEAP2, LPAR6, NCALD, OBSCN, MKI67, CPAMD8, D4S234E, SLC16A4, CRCT1, LY6D, RRM2, CIT, SC4MOL, NR1D1, ELF5, LPHN1, CRABP2, CEBPD, C6orf105, ANKRD9, C8orf4, TNFRSF18, TOR1B, TF, ACOT11, SLC35E2, LOC388564, SLC9A4, LRP12, ISYNA1, CARD11, MMP9, NCRNA00152, CROCC, CES1, TMX4, RAB3B, TNC, FOXN4, NCRNA00086, HLA.DQA1, RHCG, SLC9A3, SCGB3A1, SCD, ALOX15B, POU2F3, TCTEX1D4

TABLE 5 Characteristics of the external asthma cohorts used in the validation of the asthma gene panel. Asthmal²⁸ GEO GSE19187 Asthma2²⁹ GEO GSE46171* Class No No Asthma Asthma Asthma Asthma (n = 13) (n = 11) (n = 23) (n = 5) Definition Recurring wheezing, No personal or dyspnea, cough family No known and bronchodilator history of atopy, History of airway response rhinitis, or asthma asthma disease Control Controlled{circumflex over ( )} Uncontrolled n/a Controlled{circumflex over ( )} Uncontrolled n/a Subjects 7 6 11 16 7 5 Age-years 11.5 (3.2) 9.1 (0.6) 11.5 (3.1) 37 (19-66)† 29 (25-46)† 30 (18-37)† Female 5 (71.4%) 2 (33.3%) 4 (36.4%) 36%  20%  14%  Race Caucasian n/a n/a n/a 26%  18%  16%  African n/a n/a n/a 8% 2% 0% American Hispanic n/a n/a n/a 6% 0% 0% Other n/a n/a n/a 6% 2% 2% Rhinitis or 7 (100%) 6 (100%) 0 (0%) 36%  16%  2% atopic FEV1 97.6 (13.2) 78.2 (7.7) n/a 97.8 (16.5) 91.2 (10.8) 98.3 (11.0) % predicted FEV1/FVC 89.3 (5.6) 76.5 (3.2) n/a n/a n/a n/a PC20 (mg/ml) n/a n/a n/a 4.5 (5.1) 4.4 (5.2) 28 (27.1) Results are number (%) or mean (SD) unless otherwise indicated. {circumflex over ( )}For Asthma1, criteria for control per NAEPP/EPR3 criteria. For Asthma2, criteria for control not specified. *For Asthma2, data that the authors deposited in GEO GSE46171 are a subset of their published results.²⁹ GSE46171 has data for 16 of the 23 subjects with controlled asthma, 7 of the 11 subjects with uncontrolled asthma, and 5 of the 9 controls reported in the authors' publication.²⁹ The number of subjects with publically available data (GSE46171) that were used in these analyses are indicated. The summary statistics shown are drawn from the authors' publication on their reported sample. †Median (range).

TABLE 6 Characteristics of the external cohorts with non-asthma respiratory conditions and controls used in the validation of the asthma gene panel. Allergic Rhinitis³⁵ URI Day 2²⁹ GEO URI Day 6²⁹ GEO Cystic Fibrosis³⁶ Smoking¹¹ GEO GSE43523* GSE46171{circumflex over ( )} GSE46171 GEO GSE40445 GEO GSE8987 Class Allergic Cystic Rhinitis Control URI Control URI Control Fibrosis Control Smoking Control N = 7 N = 5 N = 6 N = 5 N = 6 N = 5 N = 5 N = 5 N = 7 N = 8 Defini- tion** Age - 37.9 (9.3) 32.9 (7.8) 30 (18-37)† 30 (18-37)† 30 (18-37)† 30 (18-37)† 14 (4.2) 14.8 (1.1) 47 (12) 43 (18) years Female 60%  38.5%   14%  14%  14%  14%  3 (60%) 2 (40%) 1 (14.3%) 2 (25%) Race Cauca- 0% 0% 16%  16%  16%  16%  5 (100%) 5 (100%) 3 (42.9%) 5 (62.5%) sian Af- 0% 0% 0% 0% 0% 0% 0% 0% 2 (28.6%) 2 (25%) Amer- ican His- 0% 0% 0% 0% 0% 0% 0% 0% 1 (14.3%) 1 (12.5%) panic Other 100%  100%  2% 2% 2% 2% 0% 0% 0 (0%) 0 (0%) *Data that the authors deposited in GEO GSE43523 are a subset of their published results.³⁵ GSE43523 has data for 7 of the 15 subjects with allergic rhinitis, and 5 of the 13 controls reported in the authors' publication.³⁵ The number of subjects with publically available data (GSE43523) that were used in these analyses are indicated. The summary statistics shown are drawn from the authors' publication on their reported cohort. {circumflex over ( )}Each subject provided a URI and control sample. The data that the authors deposited in GEO GSE46171 are a subset of their published results.²⁹ GSE46171 has data for 6 of the 9 healthy subjects reported in the authors' publication who provided samples during URI, and 5 of the 9 healthy subjects who provided samples after resolution of their URI.²⁹ The number of subjects with publically available data (GSE46171) that were used in these analyses are indicated. The summary statistics shown are drawn from the authors' publication on their reported cohort. †Median (range). **Definitions: Allergic Rhinitis = Rhinitis symptoms and ≥1 elevated sIgE to aeroallergen; Allergic rhinitis control = No symptoms, no sIgE to aeroallergen, total serum IgE < population mean. URI Day 2 = Day 2 following onset of “common cold” symptoms and no underlying airway disease; URI Day 2 control = No URI symptoms and no known airway disease. URI Day 6 = Day 6 following onset of “common cold” symptoms and no underlying airway disease; URI Day 6 control = No URI symptoms and no known airway disease. Cystic Fibrosis = Homozygous F508del mutation; Cystic Fibrosis control = Overweight but healthy. Smoking = ≥10 cigarettes/day in past month and smoking ≥10 pack years; Smoking control = Never smoker, no environmental cigarette exposure and no respiratory symptoms.

TABLE 7 Positive and negative predictive values (PPV and NPV respectively) for the LR-RFE & Logistic asthma gene panel. Non-asthma data sets PPV NPV Allergic Rhinitis 0.00 (0.51) 0.42 (0.16) URI Day 2 0.50 (0.43) 0.44 (0.22) URI Day 6 0.00 (0.43) 0.40 (0.23) Cystic Fibrosis 0.00 (0.44) 0.50 (0.27) Smoking 0.00 (0.29) 0.53 (0.36)

Positive and negative predictive values (PPV and NPV respectively) obtained when the LR-RFE & Logistic asthma gene panel was applied to classifying samples in various microarray-derived data sets of subjects with non-asthma respiratory conditions and controls. Also shown in parentheses are the corresponding PPVs and NPVs obtained when random counterpart models are applied to these datasets for the same classification tasks.

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While several possible embodiments are disclosed above, embodiments of the present invention are not so limited. These exemplary embodiments are not intended to be exhaustive or to unnecessarily limit the scope of the invention, but instead were chosen and described in order to explain the principles of the present invention so that others skilled in the art may practice the invention. Indeed, various modifications of the invention in addition to those described herein will become apparent to those skilled in the art from the foregoing description. Such modifications are intended to fall within the scope of the appended claims.

Disclosed are methods and compositions that can be used for, can be used in conjunction with, can be used in preparation for, or are products of the disclosed methods and compositions. These and other materials are disclosed herein, and it is understood that combinations, subsets, interactions, groups, etc. of these methods and compositions are disclosed.

All patents, applications, publications, test methods, literature, and other materials cited herein are hereby incorporated by reference in their entirety as if physically present in this specification. 

1. A method for diagnosing asthma in a subject, comprising the steps of: a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject; b) performing classification analysis on the gene counts obtained from the gene expression profile(s); c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.
 2. A method for detection of asthma in a subject, comprising the steps of: a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject; b) performing classification analysis on the gene counts obtained from the gene expression profile(s); c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.
 3. A method for differentially diagnosing asthma from other respiratory disorders in a subject, comprising the steps of: a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject; b) performing classification analysis on the gene counts obtained from the gene expression profile(s); c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.
 4. A method for classifying a subject as having asthma or not having asthma, comprising the steps of: a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject; b) performing classification analysis on the gene counts obtained from the gene expression profile(s); c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.
 5. A method for monitoring asthma in a subject, comprising the steps of: a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject; b) performing classification analysis on the gene counts obtained from the gene expression profile(s); c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.
 6. A method for selecting a subject for a clinical trial for asthma therapeutic compositions and/or methods, comprising the steps of: a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject; b) performing classification analysis on the gene counts obtained from the gene expression profile(s); c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; and d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold.
 7. A method for treating asthma in a subject, comprising the steps of: a) measuring the gene expression profile(s) of at least one of the genes in the asthma gene panel in a nasal swab/scraping/brushing/wash/sponge collected from the subject; b) performing classification analysis on the gene counts obtained from the gene expression profile(s); c) comparing the probability output obtained from the classification analysis to the optimal classification threshold; d) identifying the subject as (i) having asthma when the probability output is greater than or equal to the optimal classification threshold or (ii) not having asthma when the probability output is less than the optimal classification threshold; and e) utilizing appropriate therapeutic compositions and/or methods if the subject has asthma.
 8. The method as described in claim 1, wherein step (a) further comprises the steps of (i) brushing/swabbing/scraping/washing/sponging the patient's nose, (ii) obtaining and appropriately preserving the nasal brushing/swab/scraping/wash/sponge sample, and (iii) assaying the gene expression profile of the cells and tissue contained in the sample, whether by isolating RNA as described herein or by use of a RNA profiling system that does not require a separate isolation step.
 9. The method as described in claim 1, wherein the classification analysis comprises Logistic Regression-Recursive Feature Elimination (LR-RFE) algorithms in combination with Logistic algorithm, the asthma gene panel consists of the LR-RFE & Logistic asthma gene panel, and the optimal classification threshold is about 0.76.
 10. The method as described in claim 1, wherein the classification analysis comprises LR-RFE algorithm in combination with SVM-Linear algorithms, the asthma gene panel consists of the LR-RFE & SVM-Linear asthma gene panel, and the optimal classification threshold is about 0.52.
 11. The method as described in claim 1, wherein the classification analysis comprises the SVM-RFE algorithm in combination with the SVM-Linear algorithms, the asthma gene panel consists of the SVM-RFE & SVM-Linear asthma gene panel, and the optimal classification threshold is about 0.64.
 12. The method as described in claim 1, wherein the classification analysis comprises the SVM-RFE algorithm in combination with the Logistic algorithms, the asthma gene panel consists of the SVM-RFE & Logistic asthma gene panel, and the optimal classification threshold is about 0.69.
 13. The method as described in claim 1, wherein the classification analysis comprises the LR-RFE algorithm in combination with the AdaBoost algorithms, the asthma gene panel consists of the LR-RFE & AdaBoost asthma gene panel, and the optimal classification threshold is about 0.49.
 14. The method as described in claim 1, wherein the classification analysis comprises the LR-RFE algorithm in combination with the RandomForest algorithms, the asthma gene panel consists of the LR-RFE & RandomForest asthma gene panel, and the optimal classification threshold is about 0.60.
 15. The method as described in claim 1, wherein the classification analysis comprises the SVM-RFE algorithm in combination with the RandomForest algorithms, the asthma gene panel consists of the SVM-RFE & RandomForest asthma gene panel, and the optimal classification threshold is about 0.50.
 16. The method as described in claim 1, wherein the classification analysis comprises the SVM-RFE algorithm in combination with the AdaBoost algorithm, the asthma gene panel consists of the SVM-RFE & AdaBoost asthma gene panel, and the optimal classification threshold is about 0.55.
 17. The method as described in claim 1, wherein steps (b) and/or (c) and/or (d) are performed by a computer.
 18. A kit for diagnosing and/or detecting asthma in a subject, said kit comprising probes directed towards one or more of the genes in the asthma gene panel, wherein the probes can be used to determine the expression levels of one or more of the genes in the asthma gene panel.
 19. The kit of claim 12, further comprising: a detection means; an amplification means; and control probes. 